Sample records for qsar application toolbox

  1. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putativemore » sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox.

  2. Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

    PubMed

    Ataide Martins, João Paulo; Rougeth de Oliveira, Marco Antônio; Oliveira de Queiroz, Mário Sérgio

    2018-06-05

    A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

  3. Use and perceived benefits and barriers of QSAR models for REACH: findings from a questionnaire to stakeholders

    PubMed Central

    2012-01-01

    The ORCHESTRA online questionnaire on “benefits and barriers to the use of QSAR methods” addressed the academic, consultant, regulatory and industry communities potentially interested by QSAR methods in the context of REACH. Replies from more than 60 stakeholders produced some insights on the actual application of QSAR methods, and how to improve their use. Respondents state in majority that they have used QSAR methods. All have some future plans to test or use QSAR methods in accordance with their stakeholder role. The stakeholder respondents cited a total of 28 models, methods or software that they have actually applied. The three most frequently cited suites, used moreover by all the stakeholder categories, are the OECD Toolbox, EPISuite and CAESAR; all are free tools. Results suggest that stereotyped assumptions about the barriers to application of QSAR may be incorrect. Economic costs (including potential delays) are not found to be a major barrier. And only one respondent “prefers” traditional, well-known and accepted toxicological assessment methods. Information and guidance may be the keys to reinforcing use of QSAR models. Regulators appear most interested in obtaining clear explanation of the basis of the models, to provide a solid basis for decisions. Scientists appear most interested in the exploration of the scientific capabilities of the QSAR approach. Industry shows interest in obtaining reassurance that appropriate uses of QSAR will be accepted by regulators. PMID:23244245

  4. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation. PMID:25560674

  5. Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

    PubMed

    Fang, Cheng; Xiao, Zhiyan

    2016-01-01

    Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

  6. Predictive QSAR modeling workflow, model applicability domains, and virtual screening.

    PubMed

    Tropsha, Alexander; Golbraikh, Alexander

    2007-01-01

    Quantitative Structure Activity Relationship (QSAR) modeling has been traditionally applied as an evaluative approach, i.e., with the focus on developing retrospective and explanatory models of existing data. Model extrapolation was considered if only in hypothetical sense in terms of potential modifications of known biologically active chemicals that could improve compounds' activity. This critical review re-examines the strategy and the output of the modern QSAR modeling approaches. We provide examples and arguments suggesting that current methodologies may afford robust and validated models capable of accurate prediction of compound properties for molecules not included in the training sets. We discuss a data-analytical modeling workflow developed in our laboratory that incorporates modules for combinatorial QSAR model development (i.e., using all possible binary combinations of available descriptor sets and statistical data modeling techniques), rigorous model validation, and virtual screening of available chemical databases to identify novel biologically active compounds. Our approach places particular emphasis on model validation as well as the need to define model applicability domains in the chemistry space. We present examples of studies where the application of rigorously validated QSAR models to virtual screening identified computational hits that were confirmed by subsequent experimental investigations. The emerging focus of QSAR modeling on target property forecasting brings it forward as predictive, as opposed to evaluative, modeling approach.

  7. Comparison of Cramer classification between Toxtree, the OECD QSAR Toolbox and expert judgment.

    PubMed

    Bhatia, Sneha; Schultz, Terry; Roberts, David; Shen, Jie; Kromidas, Lambros; Marie Api, Anne

    2015-02-01

    The Threshold of Toxicological Concern (TTC) is a pragmatic approach in risk assessment. In the absence of data, it sets up levels of human exposure that are considered to have no appreciable risk to human health. The Cramer decision tree is used extensively to determine these exposure thresholds by categorizing non-carcinogenic chemicals into three different structural classes. Therefore, assigning an accurate Cramer class to a material is a crucial step to preserve the integrity of the risk assessment. In this study the Cramer class of over 1000 fragrance materials across diverse chemical classes were determined by using Toxtree (TT), the OECD QSAR Toolbox (TB), and expert judgment. Disconcordance was observed between TT and the TB. A total of 165 materials (16%) showed different results from the two programs. The overall concordance for Cramer classification between TT and expert judgment is 83%, while the concordance between the TB and expert judgment is 77%. Amines, lactones and heterocycles have the lowest percent agreement with expert judgment for TT and the TB. For amines, the expert judgment agreement is 45% for TT and 55% for the TB. For heterocycles, the expert judgment agreement is 55% for TT and the TB. For lactones, the expert judgment agreement is 56% for TT and 50% for the TB. Additional analyses were conducted to determine the concordance within various chemical classes. Critical checkpoints in the decision tree are identified. Strategies and guidance on determining the Cramer class for various chemical classes are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. CURRENT PRACTICES IN QSAR DEVELOPMENT AND APPLICATIONS

    EPA Science Inventory

    Current Practices in QSAR Development and Applications

    Although it is commonly assumed that the structure and properties of a single chemical determines its activity in a particular biological system, it is only through study of how biological activity varies with changes...

  9. QSAR modeling of GPCR ligands: methodologies and examples of applications.

    PubMed

    Tropsha, A; Wang, S X

    2006-01-01

    GPCR ligands represent not only one of the major classes of current drugs but the major continuing source of novel potent pharmaceutical agents. Because 3D structures of GPCRs as determined by experimental techniques are still unavailable, ligand-based drug discovery methods remain the major computational molecular modeling approaches to the analysis of growing data sets of tested GPCR ligands. This paper presents an overview of modern Quantitative Structure Activity Relationship (QSAR) modeling. We discuss the critical issue of model validation and the strategy for applying the successfully validated QSAR models to virtual screening of available chemical databases. We present several examples of applications of validated QSAR modeling approaches to GPCR ligands. We conclude with the comments on exciting developments in the QSAR modeling of GPCR ligands that focus on the study of emerging data sets of compounds with dual or even multiple activities against two or more of GPCRs.

  10. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

    PubMed

    Roy, Kunal; Mitra, Indrani

    2011-07-01

    Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

  11. An ensemble model of QSAR tools for regulatory risk assessment.

    PubMed

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0

  12. LQTA-QSAR: a new 4D-QSAR methodology.

    PubMed

    Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

    2009-06-01

    A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

  13. An ensemble model of QSAR tools for regulatory risk assessment

    DOE PAGES

    Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

    2016-09-22

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  14. GTM-Based QSAR Models and Their Applicability Domains.

    PubMed

    Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

    2015-06-01

    In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. QSAR modeling: where have you been? Where are you going to?

    PubMed

    Cherkasov, Artem; Muratov, Eugene N; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-06-26

    Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

  16. QSAR Modeling: Where have you been? Where are you going to?

    PubMed Central

    Cherkasov, Artem; Muratov, Eugene N.; Fourches, Denis; Varnek, Alexandre; Baskin, Igor I.; Cronin, Mark; Dearden, John; Gramatica, Paola; Martin, Yvonne C.; Todeschini, Roberto; Consonni, Viviana; Kuz'min, Victor E.; Cramer, Richard; Benigni, Romualdo; Yang, Chihae; Rathman, James; Terfloth, Lothar; Gasteiger, Johann; Richard, Ann; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. PMID:24351051

  17. Application of 3D-QSAR in the rational design of receptor ligands and enzyme inhibitors.

    PubMed

    Mor, Marco; Rivara, Silvia; Lodola, Alessio; Lorenzi, Simone; Bordi, Fabrizio; Plazzi, Pier Vincenzo; Spadoni, Gilberto; Bedini, Annalida; Duranti, Andrea; Tontini, Andrea; Tarzia, Giorgio

    2005-11-01

    Quantitative structure-activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure-activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.

  18. QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity.

    PubMed

    Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Estrada, Mario R

    2015-01-01

    Quantitative structure-activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

  19. Residual-QSAR. Implications for genotoxic carcinogenesis

    PubMed Central

    2011-01-01

    Introduction Both main types of carcinogenesis, genotoxic and epigenetic, were examined in the context of non-congenericity and similarity, respectively, for the structure of ligand molecules, emphasizing the role of quantitative structure-activity relationship ((Q)SAR) studies in accordance with OECD (Organization for Economic and Cooperation Development) regulations. The main purpose of this report involves electrophilic theory and the need for meaningful physicochemical parameters to describe genotoxicity by a general mechanism. Residual-QSAR Method The double or looping multiple linear correlation was examined by comparing the direct and residual structural information against the observed activity. A self-consistent equation of observed-computed activity was assumed to give maximum correlation efficiency for those situations in which the direct correlations gave non-significant statistical information. Alternatively, it was also suited to describe slow and apparently non-noticeable cancer phenomenology, with special application to non-congeneric molecules involved in genotoxic carcinogenesis. Application and Discussions The QSAR principles were systematically applied to a given pool of molecules with genotoxic activity in rats to elucidate their carcinogenic mechanisms. Once defined, the endpoint associated with ligand-DNA interaction was used to select variables that retained the main Hansch physicochemical parameters of hydrophobicity, polarizability and stericity, computed by the custom PM3 semiempirical quantum method. The trial and test sets of working molecules were established by implementing the normal Gaussian principle of activities that applies when the applicability domain is not restrained to the congeneric compounds, as in the present study. The application of the residual, self-consistent QSAR method and the factor (or average) method yielded results characterized by extremely high and low correlations, respectively, with the latter resembling

  20. Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design.

    PubMed

    Ebalunode, Jerry O; Zheng, Weifan; Tropsha, Alexander

    2011-01-01

    Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

  1. QSAR and 3D QSAR of inhibitors of the epidermal growth factor receptor

    NASA Astrophysics Data System (ADS)

    Pinto-Bazurco, Mariano; Tsakovska, Ivanka; Pajeva, Ilza

    This article reports quantitative structure-activity relationships (QSAR) and 3D QSAR models of 134 structurally diverse inhibitors of the epidermal growth factor receptor (EGFR) tyrosine kinase. Free-Wilson analysis was used to derive the QSAR model. It identified the substituents in aniline, the polycyclic system, and the substituents at the 6- and 7-positions of the polycyclic system as the most important structural features. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used in the 3D QSAR modeling. The steric and electrostatic interactions proved the most important for the inhibitory effect. Both QSAR and 3D QSAR models led to consistent results. On the basis of the statistically significant models, new structures were proposed and their inhibitory activities were predicted.

  2. 2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

    PubMed Central

    Zhao, Manman; Zheng, Linfeng; Qiu, Chun

    2017-01-01

    Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2 = 0.565 (cross-validated correlation coefficient) and r2 = 0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR. PMID:28630865

  3. The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

    PubMed

    Tseng, Yufeng J; Hopfinger, Anton J; Esposito, Emilio Xavier

    2012-01-01

    The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

  4. Broadview Radar Altimetry Toolbox

    NASA Astrophysics Data System (ADS)

    Garcia-Mondejar, Albert; Escolà, Roger; Moyano, Gorka; Roca, Mònica; Terra-Homem, Miguel; Friaças, Ana; Martinho, Fernando; Schrama, Ernst; Naeije, Marc; Ambrózio, Américo; Restano, Marco; Benveniste, Jérôme

    2017-04-01

    The universal altimetry toolbox, BRAT (Broadview Radar Altimetry Toolbox) which can read all previous and current altimetry missions' data, incorporates now the capability to read the upcoming Sentinel3 L1 and L2 products. ESA endeavoured to develop and supply this capability to support the users of the future Sentinel3 SAR Altimetry Mission. BRAT is a collection of tools and tutorial documents designed to facilitate the processing of radar altimetry data. This project started in 2005 from the joint efforts of ESA (European Space Agency) and CNES (Centre National d'Etudes Spatiales), and it is freely available at http://earth.esa.int/brat. The tools enable users to interact with the most common altimetry data formats. The BratGUI is the frontend for the powerful command line tools that are part of the BRAT suite. BRAT can also be used in conjunction with MATLAB/IDL (via reading routines) or in C/C++/Fortran via a programming API, allowing the user to obtain desired data, bypassing the dataformatting hassle. BRAT can be used simply to visualise data quickly, or to translate the data into other formats such as NetCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, shows its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific

  5. C%2B%2B tensor toolbox user manual.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Plantenga, Todd D.; Kolda, Tamara Gibson

    2012-04-01

    The C++ Tensor Toolbox is a software package for computing tensor decompositions. It is based on the Matlab Tensor Toolbox, and is particularly optimized for sparse data sets. This user manual briefly overviews tensor decomposition mathematics, software capabilities, and installation of the package. Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors in C++. The Toolbox compiles into libraries and is intended for use with custom applications written by users.

  6. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

    PubMed Central

    Cronin, Mark T D; Jaworska, Joanna S; Walker, John D; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches. PMID:12896862

  7. Complete scanpaths analysis toolbox.

    PubMed

    Augustyniak, Piotr; Mikrut, Zbigniew

    2006-01-01

    This paper presents a complete open software environment for control, data processing and assessment of visual experiments. Visual experiments are widely used in research on human perception physiology and the results are applicable to various visual information-based man-machine interfacing, human-emulated automatic visual systems or scanpath-based learning of perceptual habits. The toolbox is designed for Matlab platform and supports infra-red reflection-based eyetracker in calibration and scanpath analysis modes. Toolbox procedures are organized in three layers: the lower one, communicating with the eyetracker output file, the middle detecting scanpath events on a physiological background and the one upper consisting of experiment schedule scripts, statistics and summaries. Several examples of visual experiments carried out with use of the presented toolbox complete the paper.

  8. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

    NASA Astrophysics Data System (ADS)

    Hsieh, Jui-Hua; Wang, Xiang S.; Teotico, Denise; Golbraikh, Alexander; Tropsha, Alexander

    2008-09-01

    The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed `binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor ( kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant Ki of 135 μM. Our studies suggest that validated QSAR models could complement

  9. GOCE User Toolbox and Tutorial

    NASA Astrophysics Data System (ADS)

    Knudsen, Per; Benveniste, Jerome

    2017-04-01

    The GOCE User Toolbox GUT is a compilation of tools for the utilisation and analysis of GOCE Level 2 products.
GUT support applications in Geodesy, Oceanography and Solid Earth Physics. The GUT Tutorial provides information
and guidance in how to use the toolbox for a variety of applications. GUT consists of a series of advanced
computer routines that carry out the required computations. It may be used on Windows PCs, UNIX/Linux Workstations,
and Mac. The toolbox is supported by The GUT Algorithm Description and User Guide and The GUT
Install Guide. A set of a-priori data and models are made available as well. Without any doubt the development
of the GOCE user toolbox have played a major role in paving the way to successful use of the GOCE data for
oceanography. The GUT version 2.2 was released in April 2014 and beside some bug-fixes it adds the capability for the computation of Simple Bouguer Anomaly (Solid-Earth). During this fall a new GUT version 3 has been released. GUTv3 was further developed through a collaborative effort where the scientific communities participate aiming
on an implementation of remaining functionalities facilitating a wider span of research in the fields of Geodesy,
Oceanography and Solid earth studies.
Accordingly, the GUT version 3 has:
 - An attractive and easy to use Graphic User Interface (GUI) for the toolbox,
 - Enhance the toolbox with some further software functionalities such as to facilitate the use of gradients,
anisotropic diffusive filtering and computation of Bouguer and isostatic gravity anomalies.
 - An associated GUT VCM tool for analyzing the GOCE variance covariance matrices.

  10. GOCE User Toolbox and Tutorial

    NASA Astrophysics Data System (ADS)

    Benveniste, Jérôme; Knudsen, Per

    2016-07-01

    The GOCE User Toolbox GUT is a compilation of tools for the utilisation and analysis of GOCE Level 2 products. GUT support applications in Geodesy, Oceanography and Solid Earth Physics. The GUT Tutorial provides information and guidance in how to use the toolbox for a variety of applications. GUT consists of a series of advanced computer routines that carry out the required computations. It may be used on Windows PCs, UNIX/Linux Workstations, and Mac. The toolbox is supported by The GUT Algorithm Description and User Guide and The GUT Install Guide. A set of a-priori data and models are made available as well. Without any doubt the development of the GOCE user toolbox have played a major role in paving the way to successful use of the GOCE data for oceanography. The GUT version 2.2 was released in April 2014 and beside some bug-fixes it adds the capability for the computation of Simple Bouguer Anomaly (Solid-Earth). During this fall a new GUT version 3 has been released. GUTv3 was further developed through a collaborative effort where the scientific communities participate aiming on an implementation of remaining functionalities facilitating a wider span of research in the fields of Geodesy, Oceanography and Solid earth studies. Accordingly, the GUT version 3 has: - An attractive and easy to use Graphic User Interface (GUI) for the toolbox, - Enhance the toolbox with some further software functionalities such as to facilitate the use of gradients, anisotropic diffusive filtering and computation of Bouguer and isostatic gravity anomalies. - An associated GUT VCM tool for analyzing the GOCE variance covariance matrices.

  11. GOCE User Toolbox and Tutorial

    NASA Astrophysics Data System (ADS)

    Knudsen, Per; Benveniste, Jerome; Team Gut

    2016-04-01

    The GOCE User Toolbox GUT is a compilation of tools for the utilisation and analysis of GOCE Level 2 products.
GUT support applications in Geodesy, Oceanography and Solid Earth Physics. The GUT Tutorial provides information
and guidance in how to use the toolbox for a variety of applications. GUT consists of a series of advanced
computer routines that carry out the required computations. It may be used on Windows PCs, UNIX/Linux Workstations,
and Mac. The toolbox is supported by The GUT Algorithm Description and User Guide and The GUT
Install Guide. A set of a-priori data and models are made available as well. Without any doubt the development
of the GOCE user toolbox have played a major role in paving the way to successful use of the GOCE data for
oceanography. The GUT version 2.2 was released in April 2014 and beside some bug-fixes it adds the capability for the computation of Simple Bouguer Anomaly (Solid-Earth). During this fall a new GUT version 3 has been released. GUTv3 was further developed through a collaborative effort where the scientific communities participate aiming
on an implementation of remaining functionalities facilitating a wider span of research in the fields of Geodesy,
Oceanography and Solid earth studies.
Accordingly, the GUT version 3 has:
 - An attractive and easy to use Graphic User Interface (GUI) for the toolbox,
 - Enhance the toolbox with some further software functionalities such as to facilitate the use of gradients,
anisotropic diffusive filtering and computation of Bouguer and isostatic gravity anomalies.
 - An associated GUT VCM tool for analyzing the GOCE variance covariance matrices.

  12. ESA Atmospheric Toolbox

    NASA Astrophysics Data System (ADS)

    Niemeijer, Sander

    2017-04-01

    The ESA Atmospheric Toolbox (BEAT) is one of the ESA Sentinel Toolboxes. It consists of a set of software components to read, analyze, and visualize a wide range of atmospheric data products. In addition to the upcoming Sentinel-5P mission it supports a wide range of other atmospheric data products, including those of previous ESA missions, ESA Third Party missions, Copernicus Atmosphere Monitoring Service (CAMS), ground based data, etc. The toolbox consists of three main components that are called CODA, HARP and VISAN. CODA provides interfaces for direct reading of data from earth observation data files. These interfaces consist of command line applications, libraries, direct interfaces to scientific applications (IDL and MATLAB), and direct interfaces to programming languages (C, Fortran, Python, and Java). CODA provides a single interface to access data in a wide variety of data formats, including ASCII, binary, XML, netCDF, HDF4, HDF5, CDF, GRIB, RINEX, and SP3. HARP is a toolkit for reading, processing and inter-comparing satellite remote sensing data, model data, in-situ data, and ground based remote sensing data. The main goal of HARP is to assist in the inter-comparison of datasets. By appropriately chaining calls to HARP command line tools one can pre-process datasets such that two datasets that need to be compared end up having the same temporal/spatial grid, same data format/structure, and same physical unit. The toolkit comes with its own data format conventions, the HARP format, which is based on netcdf/HDF. Ingestion routines (based on CODA) allow conversion from a wide variety of atmospheric data products to this common format. In addition, the toolbox provides a wide range of operations to perform conversions on the data such as unit conversions, quantity conversions (e.g. number density to volume mixing ratios), regridding, vertical smoothing using averaging kernels, collocation of two datasets, etc. VISAN is a cross-platform visualization and

  13. Broadview Radar Altimetry Toolbox

    NASA Astrophysics Data System (ADS)

    Escolà, Roger; Garcia-Mondejar, Albert; Moyano, Gorka; Roca, Mònica; Terra-Homem, Miguel; Friaças, Ana; Martinho, Fernando; Schrama, Ernst; Naeije, Marc; Ambrozio, Americo; Restano, Marco; Benveniste, Jérôme

    2016-04-01

    The universal altimetry toolbox, BRAT (Broadview Radar Altimetry Toolbox) which can read all previous and current altimetry missions' data, incorporates now the capability to read the upcoming Sentinel-3 L1 and L2 products. ESA endeavoured to develop and supply this capability to support the users of the future Sentinel-3 SAR Altimetry Mission. BRAT is a collection of tools and tutorial documents designed to facilitate the processing of radar altimetry data. This project started in 2005 from the joint efforts of ESA (European Space Agency) and CNES (Centre National d'Etudes Spatiales), and it is freely available at http://earth.esa.int/brat. The tools enable users to interact with the most common altimetry data formats. The BratGUI is the front-end for the powerful command line tools that are part of the BRAT suite. BRAT can also be used in conjunction with MATLAB/IDL (via reading routines) or in C/C++/Fortran via a programming API, allowing the user to obtain desired data, bypassing the data-formatting hassle. BRAT can be used simply to visualise data quickly, or to translate the data into other formats such as NetCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, shows its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel-3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and

  14. Broadview Radar Altimetry Toolbox

    NASA Astrophysics Data System (ADS)

    Mondéjar, Albert; Benveniste, Jérôme; Naeije, Marc; Escolà, Roger; Moyano, Gorka; Roca, Mònica; Terra-Homem, Miguel; Friaças, Ana; Martinho, Fernando; Schrama, Ernst; Ambrózio, Américo; Restano, Marco

    2016-07-01

    The universal altimetry toolbox, BRAT (Broadview Radar Altimetry Toolbox) which can read all previous and current altimetry missions' data, incorporates now the capability to read the upcoming Sentinel-3 L1 and L2 products. ESA endeavoured to develop and supply this capability to support the users of the future Sentinel-3 SAR Altimetry Mission. BRAT is a collection of tools and tutorial documents designed to facilitate the processing of radar altimetry data. This project started in 2005 from the joint efforts of ESA (European Space Agency) and CNES (Centre National d'Études Spatiales), and it is freely available at http://earth.esa.int/brat. The tools enable users to interact with the most common altimetry data formats. The BratGUI is the front-end for the powerful command line tools that are part of the BRAT suite. BRAT can also be used in conjunction with MATLAB/IDL (via reading routines) or in C/C++/Fortran via a programming API, allowing the user to obtain desired data, bypassing the data-formatting hassle. BRAT can be used simply to visualise data quickly, or to translate the data into other formats such as NetCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, shows its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel-3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and

  15. QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

    PubMed Central

    2014-01-01

    Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716

  16. AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling.

    PubMed

    Dixon, Steven L; Duan, Jianxin; Smith, Ethan; Von Bargen, Christopher D; Sherman, Woody; Repasky, Matthew P

    2016-10-01

    We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.

  17. WEC Design Response Toolbox v. 1.0

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Coe, Ryan; Michelen, Carlos; Eckert-Gallup, Aubrey

    2016-03-30

    The WEC Design Response Toolbox (WDRT) is a numerical toolbox for design-response analysis of wave energy converters (WECs). The WDRT was developed during a series of efforts to better understand WEC survival design. The WDRT has been designed as a tool for researchers and developers, enabling the straightforward application of statistical and engineering methods. The toolbox includes methods for short-term extreme response, environmental characterization, long-term extreme response and risk analysis, fatigue, and design wave composition.

  18. Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR

    DTIC Science & Technology

    2005-12-31

    recognize and predict prospective toxicity among covalent -binding AChE inhibitors of potential application to nerve agent prophylaxis and...is below since many authors do not follow the 200 word limit 14. SUBJECT TERMS nerve agents , acetylcholinesterase, prophylaxis, QSAR, virtual...Report: Reliable Prescreening of Candidate NerveAgent Prophylaxes via 3D QSAR Report Title ABSTRACT Organophosphorus (OP) nerve agents are among the

  19. DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals

    DTIC Science & Technology

    2013-04-24

    DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals Vernon...datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal . We have developed...As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and

  20. Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions.

    PubMed

    Bennett, Erin R; Clausen, Jay; Linkov, Eugene; Linkov, Igor

    2009-11-01

    Reliable, up-front information on physical and biological properties of emerging materials is essential before making a decision and investment to formulate, synthesize, scale-up, test, and manufacture a new material for use in both military and civilian applications. Multiple quantitative structure-activity relationships (QSARs) software tools are available for predicting a material's physical/chemical properties and environmental effects. Even though information on emerging materials is often limited, QSAR software output is treated without sufficient uncertainty analysis. We hypothesize that uncertainty and variability in material properties and uncertainty in model prediction can be too large to provide meaningful results. To test this hypothesis, we predicted octanol water partitioning coefficients (logP) for multiple, similar compounds with limited physical-chemical properties using six different commercial logP calculators (KOWWIN, MarvinSketch, ACD/Labs, ALogP, CLogP, SPARC). Analysis was done for materials with largely uncertain properties that were similar, based on molecular formula, to military compounds (RDX, BTTN, TNT) and pharmaceuticals (Carbamazepine, Gemfibrizol). We have also compared QSAR modeling results for a well-studied pesticide and pesticide breakdown product (Atrazine, DDE). Our analysis shows variability due to structural variations of the emerging chemicals may be several orders of magnitude. The model uncertainty across six software packages was very high (10 orders of magnitude) for emerging materials while it was low for traditional chemicals (e.g. Atrazine). Thus the use of QSAR models for emerging materials screening requires extensive model validation and coupling QSAR output with available empirical data and other relevant information.

  1. Pointing System Simulation Toolbox with Application to a Balloon Mission Simulator

    NASA Technical Reports Server (NTRS)

    Maringolo Baldraco, Rosana M.; Aretskin-Hariton, Eliot D.; Swank, Aaron J.

    2017-01-01

    The development of attitude estimation and pointing-control algorithms is necessary in order to achieve high-fidelity modeling for a Balloon Mission Simulator (BMS). A pointing system simulation toolbox was developed to enable this. The toolbox consists of a star-tracker (ST) and Inertial Measurement Unit (IMU) signal generator, a UDP (User Datagram Protocol) communication le (bridge), and an indirect-multiplicative extended Kalman filter (imEKF). This document describes the Python toolbox developed and the results of its implementation in the imEKF.

  2. Evaluation of OASIS QSAR Models Using ToxCast™ in Vitro Estrogen and Androgen Receptor Binding Data and Application in an Integrated Endocrine Screening Approach

    PubMed Central

    Bhhatarai, Barun; Wilson, Daniel M.; Price, Paul S.; Marty, Sue; Parks, Amanda K.; Carney, Edward

    2016-01-01

    Background: Integrative testing strategies (ITSs) for potential endocrine activity can use tiered in silico and in vitro models. Each component of an ITS should be thoroughly assessed. Objectives: We used the data from three in vitro ToxCast™ binding assays to assess OASIS, a quantitative structure-activity relationship (QSAR) platform covering both estrogen receptor (ER) and androgen receptor (AR) binding. For stronger binders (described here as AC50 < 1 μM), we also examined the relationship of QSAR predictions of ER or AR binding to the results from 18 ER and 10 AR transactivation assays, 72 ER-binding reference compounds, and the in vivo uterotrophic assay. Methods: NovaScreen binding assay data for ER (human, bovine, and mouse) and AR (human, chimpanzee, and rat) were used to assess the sensitivity, specificity, concordance, and applicability domain of two OASIS QSAR models. The binding strength relative to the QSAR-predicted binding strength was examined for the ER data. The relationship of QSAR predictions of binding to transactivation- and pathway-based assays, as well as to in vivo uterotrophic responses, was examined. Results: The QSAR models had both high sensitivity (> 75%) and specificity (> 86%) for ER as well as both high sensitivity (92–100%) and specificity (70–81%) for AR. For compounds within the domains of the ER and AR QSAR models that bound with AC50 < 1 μM, the QSAR models accurately predicted the binding for the parent compounds. The parent compounds were active in all transactivation assays where metabolism was incorporated and, except for those compounds known to require metabolism to manifest activity, all assay platforms where metabolism was not incorporated. Compounds in-domain and predicted to bind by the ER QSAR model that were positive in ToxCast™ ER binding at AC50 < 1 μM were active in the uterotrophic assay. Conclusions: We used the extensive ToxCast™ HTS binding data set to show that OASIS ER and AR QSAR models had

  3. CheS-Mapper 2.0 for visual validation of (Q)SAR models

    PubMed Central

    2014-01-01

    Background Sound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking. Results We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints. Conclusions Visual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org. Graphical abstract Comparing actual and predicted activity values with CheS-Mapper.

  4. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    PubMed

    Lawhern, Vernon; Hairston, W David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

  5. DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals

    PubMed Central

    Lawhern, Vernon; Hairston, W. David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration. PMID:23638169

  6. From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

    PubMed Central

    Zhu, Hao

    2017-01-01

    Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

  7. GOCE User Toolbox and Tutorial

    NASA Astrophysics Data System (ADS)

    Knudsen, P.; Benveniste, J.

    2011-07-01

    The GOCE User Toolbox GUT is a compilation of tools for the utilisation and analysis of GOCE Level 2 products. GUT support applications in Geodesy, Oceanography and Solid Earth Physics. The GUT Tutorial provides information and guidance in how to use the toolbox for a variety of applications. GUT consists of a series of advanced computer routines that carry out the required computations. It may be used on Windows PCs, UNIX/Linux Workstations, and Mac. The toolbox is supported by The GUT Algorithm Description and User Guide and The GUT Install Guide. A set of a-priori data and models are made available as well. GUT has been developed in a collaboration within the GUT Core Group. The GUT Core Group: S. Dinardo, D. Serpe, B.M. Lucas, R. Floberghagen, A. Horvath (ESA), O. Andersen, M. Herceg (DTU), M.-H. Rio, S. Mulet, G. Larnicol (CLS), J. Johannessen, L.Bertino (NERSC), H. Snaith, P. Challenor (NOC), K. Haines, D. Bretherton (NCEO), C. Hughes (POL), R.J. Bingham (NU), G. Balmino, S. Niemeijer, I. Price, L. Cornejo (S&T), M. Diament, I Panet (IPGP), C.C. Tscherning (KU), D. Stammer, F. Siegismund (UH), T. Gruber (TUM),

  8. Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing.

    PubMed

    Sullivan, K M; Manuppello, J R; Willett, C E

    2014-01-01

    The development of more efficient, ethical, and effective means of assessing the effects of chemicals on human health and the environment was a lifetime goal of Gilman Veith. His work has provided the foundation for the use of chemical structure for informing toxicological assessment by regulatory agencies the world over. Veith's scientific work influenced the early development of the SAR models in use today at the US Environmental Protection Agency. He was the driving force behind the Organisation for Economic Co-operation and Development QSAR Toolbox. Veith was one of a few early pioneers whose vision led to the linkage of chemical structure and biological activity as a means of predicting adverse apical outcomes (known as a mode of action, or an adverse outcome pathway approach), and he understood at an early stage the power that could be harnessed when combining computational and mechanistic biological approaches as a means of avoiding animal testing. Through the International QSAR Foundation he organized like-minded experts to develop non-animal methods and frameworks for the assessment of chemical hazard and risk for the benefit of public and environmental health. Avoiding animal testing was Gil's passion, and his work helped to initiate the paradigm shift in toxicology that is now rendering this feasible.

  9. TOXICO-CHEMINFORMATICS AND QSAR MODELING OF ...

    EPA Pesticide Factsheets

    This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models. This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models.

  10. GridPV Toolbox

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Broderick, Robert; Quiroz, Jimmy; Grijalva, Santiago

    2014-07-15

    Matlab Toolbox for simulating the impact of solar energy on the distribution grid. The majority of the functions are useful for interfacing OpenDSS and MATLAB, and they are of generic use for commanding OpenDSS from MATLAB and retrieving GridPV Toolbox information from simulations. A set of functions is also included for modeling PV plant output and setting up the PV plant in the OpenDSS simulation. The toolbox contains functions for modeling the OpenDSS distribution feeder on satellite images with GPS coordinates. Finally, example simulations functions are included to show potential uses of the toolbox functions.

  11. DemQSAR: predicting human volume of distribution and clearance of drugs

    NASA Astrophysics Data System (ADS)

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is

  12. DemQSAR: predicting human volume of distribution and clearance of drugs.

    PubMed

    Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

    2011-12-01

    In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is

  13. Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

    PubMed

    Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

    2010-06-30

    QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but

  14. Towards interoperable and reproducible QSAR analyses: Exchange of datasets

    PubMed Central

    2010-01-01

    Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets

  15. Sentinel-2 data exploitation with ESA's Sentinel-2 Toolbox

    NASA Astrophysics Data System (ADS)

    Gascon, Ferran; Ramoino, Fabrizzio; deanos, Yves-louis

    2017-04-01

    The Sentinel-2 Toolbox is a project kicked off by ESA in early 2014, under the umbrella of the ESA SEOM programme with the aim to provide a tool for visualizing, analysing, and processing the Sentinel-2 datasets. The toolbox is an extension of the SeNtinel Application Platform (SNAP), a project resulting from the effort of the developers of the Sentinel-1, Sentinel-2 and Sentinel-3 toolbox to provide a single common application framework suited for the mixed exploitation of SAR, high resolution optical and medium resolution optical datasets. All three development teams collaborate to drive the evolution of the common SNAP framework in a developer forum. In this triplet, the Sentinel-2 toolbox is dedicated to enhance SNAP support for high resolution optical imagery. It is a multi-mission toolbox, already providing support for Sentinel-2, RapidEye, Deimos, SPOT 1 to SPOT 5 datasets. In terms of processing algorithms, SNAP provides tools specific to the Sentinel-2 mission : • An atmospheric correction module, Sen2Cor, is integrated into the toolbox, and provides scene classification, atmospheric correction, cirrus detection and correction. The output L2A products can be opened seamlessly in the toolbox. • A multitemporal synthesis processor (L3) • A biophysical products processor (L2B) • A water processor • A deforestation detector • OTB tools integration • SNAP Engine for Cloud Exploitation along with a set of more generic tools for high resolution optical data exploitation. Together with the generic functionalities of SNAP this provides an ideal environment for designing multi-missions processing chains and producing value-added products from raw datasets. The use of SNAP is manifold and the desktop tools provides a rich application for interactive visualization, analysis and processing of data. But all tools available from SNAP can be accessed via command-line through the Graph Processing Framework (GPT), the kernel of the SNAP processing engine. This

  16. Application of 3D-QSAR for identification of descriptors defining bioactivity of antimicrobial peptides.

    PubMed

    Bhonsle, Jayendra B; Venugopal, Divakaramenon; Huddler, Donald P; Magill, Alan J; Hicks, Rickey P

    2007-12-27

    In our laboratory, a series of antimicrobial peptides have been developed, where the resulting 3D-physicochemical properties are controlled by the placement of amino acids with well-defined properties (hydrophobicity, charge density, electrostatic potential, and so on) at specific locations along the peptide backbone. These peptides exhibited different in vitro activity against Staphylococcus aureus (SA) and Mycobacterium ranae (MR) bacteria. We hypothesized that the differences in the biological activity is a direct manifestation of different physicochemical interactions that occur between the peptides and the cell membranes of the bacteria. 3D-QSAR analysis has shown that, within this series, specific physicochemical properties are responsible for antibacterial activity and selectivity. There are five physicochemical properties specific to the SA QSAR model, while five properties are specific to the MR QSAR model. These results support the hypothesis that, for any particular AMP, organism selectivity and potency are controlled by the chemical composition of the target cell membrane.

  17. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

    PubMed Central

    2011-01-01

    Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of

  18. AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

    PubMed

    Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

    2011-07-28

    Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models

  19. ESA's Multi-mission Sentinel-1 Toolbox

    NASA Astrophysics Data System (ADS)

    Veci, Luis; Lu, Jun; Foumelis, Michael; Engdahl, Marcus

    2017-04-01

    The Sentinel-1 Toolbox is a new open source software for scientific learning, research and exploitation of the large archives of Sentinel and heritage missions. The Toolbox is based on the proven BEAM/NEST architecture inheriting all current NEST functionality including multi-mission support for most civilian satellite SAR missions. The project is funded through ESA's Scientific Exploitation of Operational Missions (SEOM). The Sentinel-1 Toolbox will strive to serve the SEOM mandate by providing leading-edge software to the science and application users in support of ESA's operational SAR mission as well as by educating and growing a SAR user community. The Toolbox consists of a collection of processing tools, data product readers and writers and a display and analysis application. A common architecture for all Sentinel Toolboxes is being jointly developed by Brockmann Consult, Array Systems Computing and C-S called the Sentinel Application Platform (SNAP). The SNAP architecture is ideal for Earth Observation processing and analysis due the following technological innovations: Extensibility, Portability, Modular Rich Client Platform, Generic EO Data Abstraction, Tiled Memory Management, and a Graph Processing Framework. The project has developed new tools for working with Sentinel-1 data in particular for working with the new Interferometric TOPSAR mode. TOPSAR Complex Coregistration and a complete Interferometric processing chain has been implemented for Sentinel-1 TOPSAR data. To accomplish this, a coregistration following the Spectral Diversity[4] method has been developed as well as special azimuth handling in the coherence, interferogram and spectral filter operators. The Toolbox includes reading of L0, L1 and L2 products in SAFE format, calibration and de-noising, slice product assembling, TOPSAR deburst and sub-swath merging, terrain flattening radiometric normalization, and visualization for L2 OCN products. The Toolbox also provides several new tools for

  20. A comprehensive validation toolbox for regional ocean models - Outline, implementation and application to the Baltic Sea

    NASA Astrophysics Data System (ADS)

    Jandt, Simon; Laagemaa, Priidik; Janssen, Frank

    2014-05-01

    The systematic and objective comparison between output from a numerical ocean model and a set of observations, called validation in the context of this presentation, is a beneficial activity at several stages, starting from early steps in model development and ending at the quality control of model based products delivered to customers. Even though the importance of this kind of validation work is widely acknowledged it is often not among the most popular tasks in ocean modelling. In order to ease the validation work a comprehensive toolbox has been developed in the framework of the MyOcean-2 project. The objective of this toolbox is to carry out validation integrating different data sources, e.g. time-series at stations, vertical profiles, surface fields or along track satellite data, with one single program call. The validation toolbox, implemented in MATLAB, features all parts of the validation process - ranging from read-in procedures of datasets to the graphical and numerical output of statistical metrics of the comparison. The basic idea is to have only one well-defined validation schedule for all applications, in which all parts of the validation process are executed. Each part, e.g. read-in procedures, forms a module in which all available functions of this particular part are collected. The interface between the functions, the module and the validation schedule is highly standardized. Functions of a module are set up for certain validation tasks, new functions can be implemented into the appropriate module without affecting the functionality of the toolbox. The functions are assigned for each validation task in user specific settings, which are externally stored in so-called namelists and gather all information of the used datasets as well as paths and metadata. In the framework of the MyOcean-2 project the toolbox is frequently used to validate the forecast products of the Baltic Sea Marine Forecasting Centre. Hereby the performance of any new product

  1. Integrating model behavior, optimization, and sensitivity/uncertainty analysis: overview and application of the MOUSE software toolbox

    USDA-ARS?s Scientific Manuscript database

    This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration syst...

  2. Wyrm: A Brain-Computer Interface Toolbox in Python.

    PubMed

    Venthur, Bastian; Dähne, Sven; Höhne, Johannes; Heller, Hendrik; Blankertz, Benjamin

    2015-10-01

    In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.

  3. QSAR of phytochemicals for the design of better drugs.

    PubMed

    Kar, Supratik; Roy, Kunal

    2012-10-01

    Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era. QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area. This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.

  4. Integration of QSAR and in vitro toxicology.

    PubMed Central

    Barratt, M D

    1998-01-01

    The principles of quantitative structure-activity relationships (QSAR) are based on the premise that the properties of a chemical are implicit in its molecular structure. Therefore, if a mechanistic hypothesis can be proposed linking a group of related chemicals with a particular toxic end point, the hypothesis can be used to define relevant parameters to establish a QSAR. Ways in which QSAR and in vitro toxicology can complement each other in development of alternatives to live animal experiments are described and illustrated by examples from acute toxicological end points. Integration of QSAR and in vitro methods is examined in the context of assessing mechanistic competence and improving the design of in vitro assays and the development of prediction models. The nature of biological variability is explored together with its implications for the selection of sets of chemicals for test development, optimization, and validation. Methods are described to support the use of data from in vivo tests that do not meet today's stringent requirements of acceptability. Integration of QSAR and in vitro methods into strategic approaches for the replacement, reduction, and refinement of the use of animals is described with examples. PMID:9599692

  5. Software Toolbox for Low-Frequency Conductivity and Current Density Imaging Using MRI.

    PubMed

    Sajib, Saurav Z K; Katoch, Nitish; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

    2017-11-01

    Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI. To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment. The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox. Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes. Low-frequency conductivity and current density imaging using MRI includes

  6. Compressible Flow Toolbox

    NASA Technical Reports Server (NTRS)

    Melcher, Kevin J.

    2006-01-01

    The Compressible Flow Toolbox is primarily a MATLAB-language implementation of a set of algorithms that solve approximately 280 linear and nonlinear classical equations for compressible flow. The toolbox is useful for analysis of one-dimensional steady flow with either constant entropy, friction, heat transfer, or Mach number greater than 1. The toolbox also contains algorithms for comparing and validating the equation-solving algorithms against solutions previously published in open literature. The classical equations solved by the Compressible Flow Toolbox are as follows: The isentropic-flow equations, The Fanno flow equations (pertaining to flow of an ideal gas in a pipe with friction), The Rayleigh flow equations (pertaining to frictionless flow of an ideal gas, with heat transfer, in a pipe of constant cross section), The normal-shock equations, The oblique-shock equations, and The expansion equations.

  7. Estrogen Receptor Binding Affinity of Food Contact Material Components Estimated by QSAR.

    PubMed

    Sosnovcová, Jitka; Rucki, Marián; Bendová, Hana

    2016-09-01

    The presented work characterized components of food contact materials (FCM) with potential to bind to estrogen receptor (ER) and cause adverse effects in the human organism. The QSAR Toolbox, software application designed to identify and fill toxicological data gaps for chemical hazard assessment, was used. Estrogen receptors are much less of a lock-and-key interaction than highly specific ones. The ER is nonspecific enough to permit binding with a diverse array of chemical structures. There are three primary ER binding subpockets, each with different requirements for hydrogen bonding. More than 900 compounds approved as of FCM components were evaluated for their potential to bind on ER. All evaluated chemicals were subcategorized to five groups with respect to the binding potential to ER: very strong, strong, moderate, weak binder, and no binder to ER. In total 46 compounds were characterized as potential disturbers of estrogen receptor. Among the group of selected chemicals, compounds with high and even very high affinity to the ER binding subpockets were found. These compounds may act as gene activators and cause adverse effects in the organism, particularly during pregnancy and breast-feeding. It should be considered to carry out further in vitro or in vivo tests to confirm their potential to disturb the regulation of physiological processes in humans by abnormal ER signaling and subsequently remove these chemicals from the list of approved food contact materials. Copyright© by the National Institute of Public Health, Prague 2016

  8. Genetic training of network using chaos concept: application to QSAR studies of vibration modes of tetrahedral halides.

    PubMed

    Lu, Qingzhang; Shen, Guoli; Yu, Ruqin

    2002-11-15

    The chaotic dynamical system is introduced in genetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (nu(1)(A1) and nu(2)(E)) of halides [MX(4)](epsilon). The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples. Copyright 2002 Wiley Periodicals, Inc.

  9. Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

    PubMed

    Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

    2014-02-01

    Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

  10. A CRISPR-Based Toolbox for Studying T Cell Signal Transduction

    PubMed Central

    Chi, Shen; Weiss, Arthur; Wang, Haopeng

    2016-01-01

    CRISPR/Cas9 system is a powerful technology to perform genome editing in a variety of cell types. To facilitate the application of Cas9 in mapping T cell signaling pathways, we generated a toolbox for large-scale genetic screens in human Jurkat T cells. The toolbox has three different Jurkat cell lines expressing distinct Cas9 variants, including wild-type Cas9, dCas9-KRAB, and sunCas9. We demonstrated that the toolbox allows us to rapidly disrupt endogenous gene expression at the DNA level and to efficiently repress or activate gene expression at the transcriptional level. The toolbox, in combination with multiple currently existing genome-wide sgRNA libraries, will be useful to systematically investigate T cell signal transduction using both loss-of-function and gain-of-function genetic screens. PMID:27057542

  11. The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

    PubMed

    Crosse, Michael J; Di Liberto, Giovanni M; Bednar, Adam; Lalor, Edmund C

    2016-01-01

    Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

  12. The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

    PubMed Central

    Crosse, Michael J.; Di Liberto, Giovanni M.; Bednar, Adam; Lalor, Edmund C.

    2016-01-01

    Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. PMID:27965557

  13. ESA BRAT (Broadview Radar Altimetry Toolbox) and GUT (GOCE User Toolbox) toolboxes

    NASA Astrophysics Data System (ADS)

    Benveniste, J.; Ambrozio, A.; Restano, M.

    2016-12-01

    The Broadview Radar Altimetry Toolbox (BRAT) is a collection of tools designed to facilitate the processing of radar altimetry data from previous and current altimetry missions, including the upcoming Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases. BRAT's future release (4.0.0) is planned for September 2016. Based on the community feedback, the frontend has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images. The GOCE User Toolbox (GUT) is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions. In the current 3.0 version, GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's VCM (Variance-Covariance Matrix) tool for analysing GOCE

  14. System engineering toolbox for design-oriented engineers

    NASA Technical Reports Server (NTRS)

    Goldberg, B. E.; Everhart, K.; Stevens, R.; Babbitt, N., III; Clemens, P.; Stout, L.

    1994-01-01

    This system engineering toolbox is designed to provide tools and methodologies to the design-oriented systems engineer. A tool is defined as a set of procedures to accomplish a specific function. A methodology is defined as a collection of tools, rules, and postulates to accomplish a purpose. For each concept addressed in the toolbox, the following information is provided: (1) description, (2) application, (3) procedures, (4) examples, if practical, (5) advantages, (6) limitations, and (7) bibliography and/or references. The scope of the document includes concept development tools, system safety and reliability tools, design-related analytical tools, graphical data interpretation tools, a brief description of common statistical tools and methodologies, so-called total quality management tools, and trend analysis tools. Both relationship to project phase and primary functional usage of the tools are also delineated. The toolbox also includes a case study for illustrative purposes. Fifty-five tools are delineated in the text.

  15. The Biopsychology-Toolbox: a free, open-source Matlab-toolbox for the control of behavioral experiments.

    PubMed

    Rose, Jonas; Otto, Tobias; Dittrich, Lars

    2008-10-30

    The Biopsychology-Toolbox is a free, open-source Matlab-toolbox for the control of behavioral experiments. The major aim of the project was to provide a set of basic tools that allow programming novices to control basic hardware used for behavioral experimentation without limiting the power and flexibility of the underlying programming language. The modular design of the toolbox allows portation of parts as well as entire paradigms between different types of hardware. In addition to the toolbox, this project offers a platform for the exchange of functions, hardware solutions and complete behavioral paradigms.

  16. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

    PubMed

    Nendza, Monika; Müller, Martin; Wenzel, Andrea

    2017-03-22

    Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR

  17. Neural network-based QSAR and insecticide discovery: spinetoram

    NASA Astrophysics Data System (ADS)

    Sparks, Thomas C.; Crouse, Gary D.; Dripps, James E.; Anzeveno, Peter; Martynow, Jacek; DeAmicis, Carl V.; Gifford, James

    2008-06-01

    Improvements in the efficacy and spectrum of the spinosyns, novel fermentation derived insecticide, has long been a goal within Dow AgroSciences. As large and complex fermentation products identifying specific modifications to the spinosyns likely to result in improved activity was a difficult process, since most modifications decreased the activity. A variety of approaches were investigated to identify new synthetic directions for the spinosyn chemistry including several explorations of the quantitative structure activity relationships (QSAR) of spinosyns, which initially were unsuccessful. However, application of artificial neural networks (ANN) to the spinosyn QSAR problem identified new directions for improved activity in the chemistry, which subsequent synthesis and testing confirmed. The ANN-based analogs coupled with other information on substitution effects resulting from spinosyn structure activity relationships lead to the discovery of spinetoram (XDE-175). Launched in late 2007, spinetoram provides both improved efficacy and an expanded spectrum while maintaining the exceptional environmental and toxicological profile already established for the spinosyn chemistry.

  18. QSAR models for anti-malarial activity of 4-aminoquinolines.

    PubMed

    Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

    2014-03-01

    In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

  19. Antibacterial Activity of Imidazolium-Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies.

    PubMed

    Hodyna, Diana; Kovalishyn, Vasyl; Rogalsky, Sergiy; Blagodatnyi, Volodymyr; Petko, Kirill; Metelytsia, Larisa

    2016-09-01

    Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA-RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. © 2016 John Wiley & Sons A/S.

  20. A Transcription Activator-Like Effector (TALE) Toolbox for Genome Engineering

    PubMed Central

    Sanjana, Neville E.; Cong, Le; Zhou, Yang; Cunniff, Margaret M.; Feng, Guoping; Zhang, Feng

    2013-01-01

    Transcription activator-like effectors (TALEs) are a class of naturally occurring DNA binding proteins found in the plant pathogen Xanthomonas sp. The DNA binding domain of each TALE consists of tandem 34-amino acid repeat modules that can be rearranged according to a simple cipher to target new DNA sequences. Customized TALEs can be used for a wide variety of genome engineering applications, including transcriptional modulation and genome editing. Here we describe a toolbox for rapid construction of custom TALE transcription factors (TALE-TFs) and nucleases (TALENs) using a hierarchical ligation procedure. This toolbox facilitates affordable and rapid construction of custom TALE-TFs and TALENs within one week and can be easily scaled up to construct TALEs for multiple targets in parallel. We also provide details for testing the activity in mammalian cells of custom TALE-TFs and TALENs using, respectively, qRT-PCR and Surveyor nuclease. The TALE toolbox described here will enable a broad range of biological applications. PMID:22222791

  1. Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

    PubMed Central

    Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

    2010-01-01

    Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

  2. Development of a QSAR Model for Thyroperoxidase Inhbition ...

    EPA Pesticide Factsheets

    hyroid hormones (THs) are involved in multiple biological processes and are critical modulators of fetal development. Even moderate changes in maternal or fetal TH levels can produce irreversible neurological deficits in children, such as lower IQ. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs, and inhibition of TPO by xenobiotics results in decreased TH synthesis. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to test the ToxCast Phase I and II chemicals. In the present study, we used the results from AUR-TPO to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition. The training set consisted of 898 discrete organic chemicals: 134 inhibitors and 764 non-inhibitors. A five times two-fold cross-validation of the model was performed, yielding a balanced accuracy of 78.7%. More recently, an additional ~800 chemicals were tested in the AUR-TPO assay. These data were used for a blinded external validation of the QSAR model, demonstrating a balanced accuracy of 85.7%. Overall, the cross- and external validation indicate a robust model with high predictive performance. Next, we used the QSAR model to predict 72,526 REACH pre-registered substances. The model could predict 49.5% (35,925) of the substances in its applicability domain and of these, 8,863 (24.7%) were predicted to be TPO inhibitors. Predictions from this screening can be used in a tiered approach to

  3. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Valencia, Antoni; Prous, Josep; Mora, Oscar

    As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90%more » was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation

  4. QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    PubMed Central

    Zhu, Hao; Martin, Todd M.; Ye, Lin; Sedykh, Alexander; Young, Douglas M.; Tropsha, Alexander

    2009-01-01

    Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity. PMID:19845371

  5. The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings.

    PubMed

    Koen, Joshua D; Barrett, Frederick S; Harlow, Iain M; Yonelinas, Andrew P

    2017-08-01

    Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.

  6. SAR/QSAR methods in public health practice

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Demchuk, Eugene, E-mail: edemchuk@cdc.gov; Ruiz, Patricia; Chou, Selene

    2011-07-15

    Methods of (Quantitative) Structure-Activity Relationship ((Q)SAR) modeling play an important and active role in ATSDR programs in support of the Agency mission to protect human populations from exposure to environmental contaminants. They are used for cross-chemical extrapolation to complement the traditional toxicological approach when chemical-specific information is unavailable. SAR and QSAR methods are used to investigate adverse health effects and exposure levels, bioavailability, and pharmacokinetic properties of hazardous chemical compounds. They are applied as a part of an integrated systematic approach in the development of Health Guidance Values (HGVs), such as ATSDR Minimal Risk Levels, which are used to protectmore » populations exposed to toxic chemicals at hazardous waste sites. (Q)SAR analyses are incorporated into ATSDR documents (such as the toxicological profiles and chemical-specific health consultations) to support environmental health assessments, prioritization of environmental chemical hazards, and to improve study design, when filling the priority data needs (PDNs) as mandated by Congress, in instances when experimental information is insufficient. These cases are illustrated by several examples, which explain how ATSDR applies (Q)SAR methods in public health practice.« less

  7. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    DOE PAGES

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta; ...

    2016-08-29

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

  8. Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Jagiello, Karolina; Grzonkowska, Monika; Swirog, Marta

    In this contribution, the advantages and limitations of two computational techniques that can be used for the investigation of nanoparticles activity and toxicity: classic nano-QSAR (Quantitative Structure–Activity Relationships employed for nanomaterials) and 3D nano-QSAR (three-dimensional Quantitative Structure–Activity Relationships, such us Comparative Molecular Field Analysis, CoMFA/Comparative Molecular Similarity Indices Analysis, CoMSIA analysis employed for nanomaterials) have been briefly summarized. Both approaches were compared according to the selected criteria, including: efficiency, type of experimental data, class of nanomaterials, time required for calculations and computational cost, difficulties in the interpretation. Taking into account the advantages and limitations of each method, we provide themore » recommendations for nano-QSAR modellers and QSAR model users to be able to determine a proper and efficient methodology to investigate biological activity of nanoparticles in order to describe the underlying interactions in the most reliable and useful manner.« less

  9. JWST Wavefront Control Toolbox

    NASA Technical Reports Server (NTRS)

    Shin, Shahram Ron; Aronstein, David L.

    2011-01-01

    A Matlab-based toolbox has been developed for the wavefront control and optimization of segmented optical surfaces to correct for possible misalignments of James Webb Space Telescope (JWST) using influence functions. The toolbox employs both iterative and non-iterative methods to converge to an optimal solution by minimizing the cost function. The toolbox could be used in either of constrained and unconstrained optimizations. The control process involves 1 to 7 degrees-of-freedom perturbations per segment of primary mirror in addition to the 5 degrees of freedom of secondary mirror. The toolbox consists of a series of Matlab/Simulink functions and modules, developed based on a "wrapper" approach, that handles the interface and data flow between existing commercial optical modeling software packages such as Zemax and Code V. The limitations of the algorithm are dictated by the constraints of the moving parts in the mirrors.

  10. A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

    PubMed

    Algamal, Z Y; Lee, M H

    2017-01-01

    A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

  11. PV_LIB Toolbox

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    2012-09-11

    While an organized source of reference information on PV performance modeling is certainly valuable, there is nothing to match the availability of actual examples of modeling algorithms being used in practice. To meet this need, Sandia has developed a PV performance modeling toolbox (PV_LIB) for Matlab. It contains a set of well-documented, open source functions and example scripts showing the functions being used in practical examples. This toolbox is meant to help make the multi-step process of modeling a PV system more transparent and provide the means for model users to validate and understand the models they use and ormore » develop. It is fully integrated into Matlab's help and documentation utilities. The PV_LIB Toolbox provides more than 30 functions that are sorted into four categories« less

  12. Application of quantitative structure activity relationship (QSAR) models to predict ozone toxicity in the lung.

    PubMed

    Kafoury, Ramzi M; Huang, Ming-Ju

    2005-08-01

    The sequence of events leading to ozone-induced airway inflammation is not well known. To elucidate the molecular and cellular events underlying ozone toxicity in the lung, we hypothesized that lipid ozonation products (LOPs) generated by the reaction of ozone with unsaturated fatty acids in the epithelial lining fluid and cell membranes play a key role in mediating ozone-induced airway inflammation. To test our hypothesis, we ozonized 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC) and generated LOPs. Confluent human bronchial epithelial cells were exposed to the derivatives of ozonized POPC-9-oxononanoyl, 9-hydroxy-9-hydroperoxynonanoyl, and 8-(5-octyl-1,2,4-trioxolan-3-yl-)octanoyl-at a concentration of 10 muM, and the activity of phospholipases A2 (PLA2), C (PLC), and D (PLD) was measured (1, 0.5, and 1 h, respectively). Quantitative structure-activity relationship (QSAR) models were utilized to predict the biological activity of LOPs in airway epithelial cells. The QSAR results showed a strong correlation between experimental and computed activity (r = 0.97, 0.98, 0.99, for PLA2, PLC, and PLD, respectively). The results indicate that QSAR models can be utilized to predict the biological activity of the various ozone-derived LOP species in the lung. Copyright 2005 Wiley Periodicals, Inc.

  13. Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

    PubMed Central

    Myint, Kyaw Z.; Xie, Xiang-Qun

    2015-01-01

    This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

  14. The Handover Toolbox: a knowledge exchange and training platform for improving patient care.

    PubMed

    Drachsler, Hendrik; Kicken, Wendy; van der Klink, Marcel; Stoyanov, Slavi; Boshuizen, Henny P A; Barach, Paul

    2012-12-01

    Safe and effective patient handovers remain a global organisational and training challenge. Limited evidence supports available handover training programmes. Customisable training is a promising approach to improve the quality and sustainability of handover training and outcomes. We present a Handover Toolbox designed in the context of the European HANDOVER Project. The Toolbox aims to support physicians, nurses, individuals in health professions training, medical educators and handover experts by providing customised handover training tools for different clinical needs and contexts. The Handover Toolbox uses the Technology Enhanced Learning Design Process (TEL-DP), which encompasses user requirements analysis; writing personas; group concept mapping; analysis of suitable software; plus, minus, interesting rating; and usability testing. TEL-DP is aligned with participatory design approaches and ensures development occurs in close collaboration with, and engagement of, key stakeholders. Application of TEL-DP confirmed that the ideal formats of handover training differs for practicing professionals versus individuals in health profession education programmes. Training experts from different countries differed in their views on the optimal content and delivery of training. Analysis of suitable software identified ready-to-use systems that provide required functionalities and can be further customised to users' needs. Interest rating and usability testing resulted in improved usability, navigation and uptake of the Handover Toolbox. The design of the Handover Toolbox was based on a carefully led stakeholder participatory design using the TEL-DP approach. The Toolbox supports a customisable learning approach that allows trainers to design training that addresses the specific information needs of the various target groups. We offer recommendations regarding the application of the Handover Toolbox to medical educators.

  15. Recent advances of molecular toolbox construction expand Pichia pastoris in synthetic biology applications.

    PubMed

    Kang, Zhen; Huang, Hao; Zhang, Yunfeng; Du, Guocheng; Chen, Jian

    2017-01-01

    Pichia pastoris: (reclassified as Komagataella phaffii), a methylotrophic yeast strain has been widely used for heterologous protein production because of its unique advantages, such as readily achievable high-density fermentation, tractable genetic modifications and typical eukaryotic post-translational modifications. More recently, P. pastoris as a metabolic pathway engineering platform has also gained much attention. In this mini-review, we addressed recent advances of molecular toolboxes, including synthetic promoters, signal peptides, and genome engineering tools that established for P. pastoris. Furthermore, the applications of P. pastoris towards synthetic biology were also discussed and prospected especially in the context of genome-scale metabolic pathway analysis.

  16. Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

    PubMed

    Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

    2013-06-01

    Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

  17. QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines

    NASA Astrophysics Data System (ADS)

    Masand, Vijay H.; El-Sayed, Nahed N. E.; Mahajan, Devidas T.; Mercader, Andrew G.; Alafeefy, Ahmed M.; Shibi, I. G.

    2017-02-01

    In the present work, sixty substituted 2-Phenylimidazopyridines previously reported with potent anti-human African trypanosomiasis (HAT) activity were selected to build genetic algorithm (GA) based QSAR models to determine the structural features that have significant correlation with the activity. Multiple QSAR models were built using easily interpretable descriptors that are directly associated with the presence or the absence of a structural scaffold, or a specific atom. All the QSAR models have been thoroughly validated according to the OECD principles. All the QSAR models are statistically very robust (R2 = 0.80-0.87) with high external predictive ability (CCCex = 0.81-0.92). The QSAR analysis reveals that the HAT activity has good correlation with the presence of five membered rings in the molecule.

  18. Automated workflows for data curation and standardization of chemical structures for QSAR modeling

    EPA Science Inventory

    Large collections of chemical structures and associated experimental data are publicly available, and can be used to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experime...

  19. The Brain's Versatile Toolbox.

    ERIC Educational Resources Information Center

    Pinker, Steven

    1997-01-01

    Considers the role of evolution and natural selection in the functioning of the modern human brain. Natural selection equipped humans with a mental toolbox of intuitive theories about the world which were used to master rocks, tools, plants, animals, and one another. The same toolbox is used today to master the intellectual challenges of modern…

  20. Combinatorial QSAR Modeling of Rat Acute Toxicity by Oral Exposure

    EPA Science Inventory

    Quantitative Structure-Activity Relationship (QSAR) toxicity models have become popular tools for identifying potential toxic compounds and prioritizing candidates for animal toxicity tests. However, few QSAR studies have successfully modeled large, diverse mammalian toxicity end...

  1. ObsPy - A Python Toolbox for Seismology - and Applications

    NASA Astrophysics Data System (ADS)

    Krischer, L.; Megies, T.; Barsch, R.; MacCarthy, J.; Lecocq, T.; Koymans, M. R.; Carothers, L.; Eulenfeld, T.; Reyes, C. G.; Falco, N.; Sales de Andrade, E.

    2017-12-01

    Recent years witnessed the evolution of Python's ecosystem into one of the most powerful and productive scientific environments across disciplines. ObsPy (https://www.obspy.org) is a fully community driven, open-source project dedicated to provide a bridge for seismology into that ecosystem. It is a Python toolbox offering: Read and write support for essentially every commonly used data format in seismology with a unified interface and automatic format detection. This includes waveform data (MiniSEED, SAC, SEG-Y, Reftek, …) as well as station (SEED, StationXML, SC3ML, …) and event meta information (QuakeML, ZMAP, …). Integrated access to the largest data centers, web services, and real-time data streams (FDSNWS, ArcLink, SeedLink, ...). A powerful signal processing toolbox tuned to the specific needs of seismologists. Utility functionality like travel time calculations with the TauP method, geodetic functions, and data visualizations. ObsPy has been in constant development for more than eight years and is developed and used by scientists around the world with successful applications in all branches of seismology. Additionally it nowadays serves as the foundation for a large number of more specialized packages. Newest features include: Full interoperability of SEED and StationXML/Inventory objects Access to the Nominal Response Library (NRL) for easy and quick creation of station metadata from scratch Support for the IRIS Federated Catalog Service Improved performance of the EarthWorm client Several improvements to MiniSEED read/write module Improved plotting capabilities for PPSD (spectrograms, PSD of discrete frequencies over time, ..) Support for.. Reading ArcLink Inventory XML Reading Reftek data format Writing SeisComp3 ML (SC3ML) Writing StationTXT format This presentation will give a short overview of the capabilities of ObsPy and point out several representative or new use cases and show-case some projects that are based on ObsPy, e.g.: seismo

  2. QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

    PubMed

    Basant, Nikita; Gupta, Shikha

    2017-06-01

    The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

  3. HYDRORECESSION: A toolbox for streamflow recession analysis

    NASA Astrophysics Data System (ADS)

    Arciniega, S.

    2015-12-01

    Streamflow recession curves are hydrological signatures allowing to study the relationship between groundwater storage and baseflow and/or low flows at the catchment scale. Recent studies have showed that streamflow recession analysis can be quite sensitive to the combination of different models, extraction techniques and parameter estimation methods. In order to better characterize streamflow recession curves, new methodologies combining multiple approaches have been recommended. The HYDRORECESSION toolbox, presented here, is a Matlab graphical user interface developed to analyse streamflow recession time series with the support of different tools allowing to parameterize linear and nonlinear storage-outflow relationships through four of the most useful recession models (Maillet, Boussinesq, Coutagne and Wittenberg). The toolbox includes four parameter-fitting techniques (linear regression, lower envelope, data binning and mean squared error) and three different methods to extract hydrograph recessions segments (Vogel, Brutsaert and Aksoy). In addition, the toolbox has a module that separates the baseflow component from the observed hydrograph using the inverse reservoir algorithm. Potential applications provided by HYDRORECESSION include model parameter analysis, hydrological regionalization and classification, baseflow index estimates, catchment-scale recharge and low-flows modelling, among others. HYDRORECESSION is freely available for non-commercial and academic purposes.

  4. QSAR studies in the discovery of novel type-II diabetic therapies.

    PubMed

    Abuhammad, Areej; Taha, Mutasem O

    2016-01-01

    Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity. This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds. Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

  5. A toolbox for safety instrumented system evaluation based on improved continuous-time Markov chain

    NASA Astrophysics Data System (ADS)

    Wardana, Awang N. I.; Kurniady, Rahman; Pambudi, Galih; Purnama, Jaka; Suryopratomo, Kutut

    2017-08-01

    Safety instrumented system (SIS) is designed to restore a plant into a safe condition when pre-hazardous event is occur. It has a vital role especially in process industries. A SIS shall be meet with safety requirement specifications. To confirm it, SIS shall be evaluated. Typically, the evaluation is calculated by hand. This paper presents a toolbox for SIS evaluation. It is developed based on improved continuous-time Markov chain. The toolbox supports to detailed approach of evaluation. This paper also illustrates an industrial application of the toolbox to evaluate arch burner safety system of primary reformer. The results of the case study demonstrates that the toolbox can be used to evaluate industrial SIS in detail and to plan the maintenance strategy.

  6. MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

    PubMed Central

    Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

    2017-01-01

    Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies. PMID:29163006

  7. MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG.

    PubMed

    Dong, Li; Li, Fali; Liu, Qiang; Wen, Xin; Lai, Yongxiu; Xu, Peng; Yao, Dezhong

    2017-01-01

    Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

  8. (Q)SARs to predict environmental toxicities: current status and future needs.

    PubMed

    Cronin, Mark T D

    2017-03-22

    The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.

  9. Quantum chemical parameters in QSAR: what do I use when?

    USGS Publications Warehouse

    Hickey, James P.; Ostrander, Gary K.

    1996-01-01

    This chapter provides a brief overview of the numerous quantum chemical parameters that have been/are currently being used in quantitative structure activity relationships (QSAR), along with a representative bibliography. The parameters will be grouped according to their mechanistic interpretations, and representative biological and physical chemical applications will be mentioned. Parmater computation methods and the appropriate software are highlighted, as are sources for software.

  10. Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

    NASA Astrophysics Data System (ADS)

    Chung, K. K.; Do, D. Q.

    2010-09-01

    In order to model relationships between chemical structures and biological effects in quantitative structure-activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data.

  11. QSAR modelling using combined simple competitive learning networks and RBF neural networks.

    PubMed

    Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E

    2018-04-01

    The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

  12. The GMT/MATLAB Toolbox

    NASA Astrophysics Data System (ADS)

    Wessel, Paul; Luis, Joaquim F.

    2017-02-01

    The GMT/MATLAB toolbox is a basic interface between MATLAB® (or Octave) and GMT, the Generic Mapping Tools, which allows MATLAB users full access to all GMT modules. Data may be passed between the two programs using intermediate MATLAB structures that organize the metadata needed; these are produced when GMT modules are run. In addition, standard MATLAB matrix data can be used directly as input to GMT modules. The toolbox improves interoperability between two widely used tools in the geosciences and extends the capability of both tools: GMT gains access to the powerful computational capabilities of MATLAB while the latter gains the ability to access specialized gridding algorithms and can produce publication-quality PostScript-based illustrations. The toolbox is available on all platforms and may be downloaded from the GMT website.

  13. Integrated system dynamics toolbox for water resources planning.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Reno, Marissa Devan; Passell, Howard David; Malczynski, Leonard A.

    2006-12-01

    Public mediated resource planning is quickly becoming the norm rather than the exception. Unfortunately, supporting tools are lacking that interactively engage the public in the decision-making process and integrate over the myriad values that influence water policy. In the pages of this report we document the first steps toward developing a specialized decision framework to meet this need; specifically, a modular and generic resource-planning ''toolbox''. The technical challenge lies in the integration of the disparate systems of hydrology, ecology, climate, demographics, economics, policy and law, each of which influence the supply and demand for water. Specifically, these systems, their associatedmore » processes, and most importantly the constitutive relations that link them must be identified, abstracted, and quantified. For this reason, the toolbox forms a collection of process modules and constitutive relations that the analyst can ''swap'' in and out to model the physical and social systems unique to their problem. This toolbox with all of its modules is developed within the common computational platform of system dynamics linked to a Geographical Information System (GIS). Development of this resource-planning toolbox represents an important foundational element of the proposed interagency center for Computer Aided Dispute Resolution (CADRe). The Center's mission is to manage water conflict through the application of computer-aided collaborative decision-making methods. The Center will promote the use of decision-support technologies within collaborative stakeholder processes to help stakeholders find common ground and create mutually beneficial water management solutions. The Center will also serve to develop new methods and technologies to help federal, state and local water managers find innovative and balanced solutions to the nation's most vexing water problems. The toolbox is an important step toward achieving the technology development goals of

  14. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.

    PubMed

    Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-06-15

    The identification of potential endocrine disrupting (ED) chemicals is an important task for the scientific community due to their diffusion in the environment; the production and use of such compounds will be strictly regulated through the authorization process of the REACH regulation. To overcome the problem of insufficient experimental data, the quantitative structure-activity relationship (QSAR) approach is applied to predict the ED activity of new chemicals. In the present study QSAR classification models are developed, according to the OECD principles, to predict the ED potency for a class of emerging ubiquitary pollutants, viz. brominated flame retardants (BFRs). Different endpoints related to ED activity (i.e. aryl hydrocarbon receptor agonism and antagonism, estrogen receptor agonism and antagonism, androgen and progesterone receptor antagonism, T4-TTR competition, E2SULT inhibition) are modeled using the k-NN classification method. The best models are selected by maximizing the sensitivity and external predictive ability. We propose simple QSARs (based on few descriptors) characterized by internal stability, good predictive power and with a verified applicability domain. These models are simple tools that are applicable to screen BFRs in relation to their ED activity, and also to design safer alternatives, in agreement with the requirements of REACH regulation at the authorization step. Copyright © 2011 Elsevier B.V. All rights reserved.

  15. MOEMS Modeling Using the Geometrical Matrix Toolbox

    NASA Technical Reports Server (NTRS)

    Wilson, William C.; Atkinson, Gary M.

    2005-01-01

    New technologies such as MicroOptoElectro-Mechanical Systems (MOEMS) require new modeling tools. These tools must simultaneously model the optical, electrical, and mechanical domains and the interactions between these domains. To facilitate rapid prototyping of these new technologies an optical toolbox has been developed for modeling MOEMS devices. The toolbox models are constructed using MATLAB's dynamical simulator, Simulink. Modeling toolboxes will allow users to focus their efforts on system design and analysis as opposed to developing component models. This toolbox was developed to facilitate rapid modeling and design of a MOEMS based laser ultrasonic receiver system.

  16. Testing adaptive toolbox models: a Bayesian hierarchical approach.

    PubMed

    Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

    2013-01-01

    Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

  17. A QSAR Model for Thyroperoxidase Inhibition and Screening ...

    EPA Pesticide Factsheets

    Thyroid hormones (THs) are critical modulators of a wide range of biological processes from neurodevelopment to metabolism. Well regulated levels of THs are critical during development and even moderate changes in maternal or fetal TH levels produce irreversible neurological deficits in children. The enzyme thyroperoxidase (TPO) plays a key role in the synthesis of THs. Inhibition of TPO by xenobiotics leads to decreased TH synthesis and, depending on the degree of synthesis inhibition, may result in adverse developmental outcomes. Recently, a high-throughput screening assay for TPO inhibition (AUR-TPO) was developed and used to screen the ToxCast Phase I and II chemicals. In the present study, we used the results from the AUR-TPO screening to develop a Quantitative Structure-Activity Relationship (QSAR) model for TPO inhibition in Leadscope®. The training set consisted of 898 discrete organic chemicals: 134 positive and 764 negative for TPO inhibition. A 10 times two-fold 50% cross-validation of the model was performed, yielding a balanced accuracy of 78.7% within its defined applicability domain. More recently, an additional ~800 chemicals from the US EPA Endocrine Disruption Screening Program (EDSP21) were screened using the AUR-TPO assay. This data was used for external validation of the QSAR model, demonstrating a balanced accuracy of 85.7% within its applicability domain. Overall, the cross- and external validations indicate a model with a high predictiv

  18. Speed management toolbox for rural communities.

    DOT National Transportation Integrated Search

    2013-04-01

    The primary objective of this toolbox is to summarize various known traffic-calming treatments and their effectiveness. This toolbox focuses on roadway-based treatments for speed management, particularly for rural communities with transition zones. E...

  19. Tensor Toolbox for MATLAB v. 3.0

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kola, Tamara; Bader, Brett W.; Acar Ataman, Evrim NMN

    Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors using MATLAB's object-oriented features. It also provides algorithms for tensor decomposition and factorization, algorithms for computing tensor eigenvalues, and methods for visualization of results.

  20. Proteins QSAR with Markov average electrostatic potentials.

    PubMed

    González-Díaz, Humberto; Uriarte, Eugenio

    2005-11-15

    Classic physicochemical and topological indices have been largely used in small molecules QSAR but less in proteins QSAR. In this study, a Markov model is used to calculate, for the first time, average electrostatic potentials xik for an indirect interaction between aminoacids placed at topologic distances k within a given protein backbone. The short-term average stochastic potential xi1 for 53 Arc repressor mutants was used to model the effect of Alanine scanning on thermal stability. The Arc repressor is a model protein of relevance for biochemical studies on bioorganics and medicinal chemistry. A linear discriminant analysis model developed correctly classified 43 out of 53, 81.1% of proteins according to their thermal stability. More specifically, the model classified 20/28, 71.4% of proteins with near wild-type stability and 23/25, 92.0% of proteins with reduced stability. Moreover, predictability in cross-validation procedures was of 81.0%. Expansion of the electrostatic potential in the series xi0, xi1, xi2, and xi3, justified the use of the abrupt truncation approach, being the overall accuracy >70.0% for xi0 but equal for xi1, xi2, and xi3. The xi1 model compared favorably with respect to others based on D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, with less than 77.0% of accuracy [Ramos de Armas, R.; González-Díaz, H.; Molina, R.; Uriarte, E. Protein Struct. Func. Bioinf.2004, 56, 715]. The xi1 model also has more tractable interpretation than others based on Markovian negentropies and stochastic moments. Finally, the model is notably simpler than the two models based on quadratic and linear indices. Both models, reported by Marrero-Ponce et al., use four-to-five time more descriptors. Introduction of average stochastic potentials may be useful for QSAR applications; having xik amenable physical interpretation and being very effective.

  1. Sentinel-3 SAR Altimetry Toolbox

    NASA Astrophysics Data System (ADS)

    Benveniste, Jerome; Lucas, Bruno; DInardo, Salvatore

    2015-04-01

    ) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, showing its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel-3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the Sentinel-3 SAR Altimetry Toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific "use-cases" for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The Kick Off is expected to be happen in Q1 2015 and have the 1st version available before the launch of Sentinel-3.

  2. A review on principles, theory and practices of 2D-QSAR.

    PubMed

    Roy, Kunal; Das, Rudra Narayan

    2014-01-01

    The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase "2D-QSAR" signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

  3. A combined QSAR and partial order ranking approach to risk assessment.

    PubMed

    Carlsen, L

    2006-04-01

    QSAR generated data appear as an attractive alternative to experimental data as foreseen in the proposed new chemicals legislation REACH. A preliminary risk assessment for the aquatic environment can be based on few factors, i.e. the octanol-water partition coefficient (Kow), the vapour pressure (VP) and the potential biodegradability of the compound in combination with the predicted no-effect concentration (PNEC) and the actual tonnage in which the substance is produced. Application of partial order ranking, allowing simultaneous inclusion of several parameters leads to a mutual prioritisation of the investigated substances, the prioritisation possibly being further analysed through the concept of linear extensions and average ranks. The ranking uses endpoint values (log Kow and log VP) derived from strictly linear 'noise-deficient' QSAR models as input parameters. Biodegradation estimates were adopted from the BioWin module of the EPI Suite. The population growth impairment of Tetrahymena pyriformis was used as a surrogate for fish lethality.

  4. The discovery of indicator variables for QSAR using inductive logic programming

    NASA Astrophysics Data System (ADS)

    King, Ross D.; Srinivasan, Ashwin

    1997-11-01

    A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.

  5. A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors.

    PubMed

    Dong, Xialan; Ebalunode, Jerry O; Cho, Sung Jin; Zheng, Weifan

    2010-02-22

    Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

  6. The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

    NASA Astrophysics Data System (ADS)

    Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

    2017-12-01

    The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

  7. The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

    NASA Astrophysics Data System (ADS)

    Kuz'min, Victor E.; Muratov, Eugene N.; Artemenko, Anatoly G.; Gorb, Leonid; Qasim, Mohammad; Leszczynski, Jerzy

    2008-10-01

    The present study applies the Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD50) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory ( R 2 = 0.96-0.98; Q 2 = 0.91-0.93; R 2 test = 0.89-0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C-H fragments of the aromatic ring.

  8. 20180318 - Automated workflows for data curation and standardization of chemical structures for QSAR modeling (ACS Spring)

    EPA Science Inventory

    Large collections of chemical structures and associated experimental data are publicly available, and can be used to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experime...

  9. A novel toolbox for E. coli lysis monitoring.

    PubMed

    Rajamanickam, Vignesh; Wurm, David; Slouka, Christoph; Herwig, Christoph; Spadiut, Oliver

    2017-01-01

    The bacterium Escherichia coli is a well-studied recombinant host organism with a plethora of applications in biotechnology. Highly valuable biopharmaceuticals, such as antibody fragments and growth factors, are currently being produced in E. coli. However, the high metabolic burden during recombinant protein production can lead to cell death, consequent lysis, and undesired product loss. Thus, fast and precise analyzers to monitor E. coli bioprocesses and to retrieve key process information, such as the optimal time point of harvest, are needed. However, such reliable monitoring tools are still scarce to date. In this study, we cultivated an E. coli strain producing a recombinant single-chain antibody fragment in the cytoplasm. In bioreactor cultivations, we purposely triggered cell lysis by pH ramps. We developed a novel toolbox using UV chromatograms as fingerprints and chemometric techniques to monitor these lysis events and used flow cytometry (FCM) as reference method to quantify viability offline. Summarizing, we were able to show that a novel toolbox comprising HPLC chromatogram fingerprinting and data science tools allowed the identification of E. coli lysis in a fast and reliable manner. We are convinced that this toolbox will not only facilitate E. coli bioprocess monitoring but will also allow enhanced process control in the future.

  10. [Application of Kohonen Self-Organizing Feature Maps in QSAR of human ADMET and kinase data sets].

    PubMed

    Hegymegi-Barakonyi, Bálint; Orfi, László; Kéri, György; Kövesdi, István

    2013-01-01

    QSAR predictions have been proven very useful in a large number of studies for drug design, such as kinase inhibitor design as targets for cancer therapy, however the overall predictability often remains unsatisfactory. To improve predictability of ADMET features and kinase inhibitory data, we present a new method using Kohonen's Self-Organizing Feature Map (SOFM) to cluster molecules based on explanatory variables (X) and separate dissimilar ones. We calculated SOFM clusters for a large number of molecules with human ADMET and kinase inhibitory data, and we showed that chemically similar molecules were in the same SOFM cluster, and within such clusters the QSAR models had significantly better predictability. We used also target variables (Y, e.g. ADMET) jointly with X variables to create a novel type of clustering. With our method, cells of loosely coupled XY data could be identified and separated into different model building sets.

  11. Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.

    PubMed

    Zhou, Peng; Wang, Congcong; Tian, Feifei; Ren, Yanrong; Yang, Chao; Huang, Jian

    2013-01-01

    Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.

  12. Acrylamide mitigation strategies: critical appraisal of the FoodDrinkEurope toolbox.

    PubMed

    Palermo, M; Gökmen, V; De Meulenaer, B; Ciesarová, Z; Zhang, Y; Pedreschi, F; Fogliano, V

    2016-06-15

    FoodDrinkEurope Federation recently released the latest version of the Acrylamide Toolbox to support manufacturers in acrylamide reduction activities giving indication about the possible mitigation strategies. The Toolbox is intended for small and medium size enterprises with limited R&D resources, however no comments about the pro and cons of the different measures were provided to advise the potential users. Experts of the field are aware that not all the strategies proposed have equal value in terms of efficacy and cost/benefit ratio. This consideration prompted us to provide a qualitative science-based ranking of the mitigation strategies proposed in the acrylamide Toolbox, focusing on bakery and fried potato products. Five authors from different geographical areas having a publication record on acrylamide mitigation strategies worked independently ranking the efficacy of the acrylamide mitigation strategies taking into account three key parameters: (i) reduction rate; (ii) side effects; and (iii) applicability and economic impact. On the basis of their own experience and considering selected literature of the last ten years, the authors scored for each key parameter the acrylamide mitigation strategies proposed in the Toolbox. As expected, all strategies selected in the Toolbox turned out to be useful, however, not at the same level. The use of enzyme asparaginase and the selection of low sugar varieties were considered the best mitigation strategies in bakery and in potato products, respectively. According to authors' opinion most of the other mitigation strategies, although effective, either have relevant side effects on the sensory profile of the products, or they are not easy to implement in industrial production. The final outcome was a science based commented ranking which can enrich the acrylamide Toolbox supporting individual manufacturer in taking the best actions to reduce the acrylamide content in their specific production context.

  13. Distributed Aerodynamic Sensing and Processing Toolbox

    NASA Technical Reports Server (NTRS)

    Brenner, Martin; Jutte, Christine; Mangalam, Arun

    2011-01-01

    A Distributed Aerodynamic Sensing and Processing (DASP) toolbox was designed and fabricated for flight test applications with an Aerostructures Test Wing (ATW) mounted under the fuselage of an F-15B on the Flight Test Fixture (FTF). DASP monitors and processes the aerodynamics with the structural dynamics using nonintrusive, surface-mounted, hot-film sensing. This aerodynamic measurement tool benefits programs devoted to static/dynamic load alleviation, body freedom flutter suppression, buffet control, improvement of aerodynamic efficiency through cruise control, supersonic wave drag reduction through shock control, etc. This DASP toolbox measures local and global unsteady aerodynamic load distribution with distributed sensing. It determines correlation between aerodynamic observables (aero forces) and structural dynamics, and allows control authority increase through aeroelastic shaping and active flow control. It offers improvements in flutter suppression and, in particular, body freedom flutter suppression, as well as aerodynamic performance of wings for increased range/endurance of manned/ unmanned flight vehicles. Other improvements include inlet performance with closed-loop active flow control, and development and validation of advanced analytical and computational tools for unsteady aerodynamics.

  14. The conservation physiology toolbox: status and opportunities

    PubMed Central

    Love, Oliver P; Hultine, Kevin R

    2018-01-01

    Abstract For over a century, physiological tools and techniques have been allowing researchers to characterize how organisms respond to changes in their natural environment and how they interact with human activities or infrastructure. Over time, many of these techniques have become part of the conservation physiology toolbox, which is used to monitor, predict, conserve, and restore plant and animal populations under threat. Here, we provide a summary of the tools that currently comprise the conservation physiology toolbox. By assessing patterns in articles that have been published in ‘Conservation Physiology’ over the past 5 years that focus on introducing, refining and validating tools, we provide an overview of where researchers are placing emphasis in terms of taxa and physiological sub-disciplines. Although there is certainly diversity across the toolbox, metrics of stress physiology (particularly glucocorticoids) and studies focusing on mammals have garnered the greatest attention, with both comprising the majority of publications (>45%). We also summarize the types of validations that are actively being completed, including those related to logistics (sample collection, storage and processing), interpretation of variation in physiological traits and relevance for conservation science. Finally, we provide recommendations for future tool refinement, with suggestions for: (i) improving our understanding of the applicability of glucocorticoid physiology; (ii) linking multiple physiological and non-physiological tools; (iii) establishing a framework for plant conservation physiology; (iv) assessing links between environmental disturbance, physiology and fitness; (v) appreciating opportunities for validations in under-represented taxa; and (vi) emphasizing tool validation as a core component of research programmes. Overall, we are confident that conservation physiology will continue to increase its applicability to more taxa, develop more non

  15. In silico study of in vitro GPCR assays by QSAR modeling ...

    EPA Pesticide Factsheets

    The U.S. EPA is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays (the ToxCast program). One goal is to prioritize chemicals for more detailed analyses based on activity in molecular initiating events (MIE) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in silico methods, and quantitative structure-activity relationships (QSARs) are a proven and cost effective approach to predict biological activity. ToxCast in turn provides relatively large datasets that are ideal for training and testing QSAR models. The overall goal of the study described here was to develop QSAR models to fill the data gaps in a larger environmental database of ~32k structures. The specific aim of the current work was to build QSAR models for 18 G-Protein Coupled Receptor (GPCR) assays, part of the aminergic category. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least squares d

  16. The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

    PubMed

    Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

    2016-10-01

    The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of

  17. Sensitivity Analysis of QSAR Models for Assessing Novel Military Compounds

    DTIC Science & Technology

    2009-01-01

    ER D C TR -0 9 -3 Strategic Environmental Research and Development Program Sensitivity Analysis of QSAR Models for Assessing Novel...Environmental Research and Development Program ERDC TR-09-3 January 2009 Sensitivity Analysis of QSAR Models for Assessing Novel Military Compound...Jay L. Clausen Cold Regions Research and Engineering Laboratory U.S. Army Engineer Research and Development Center 72 Lyme Road Hanover, NH

  18. OPERA: A free and open source QSAR tool for predicting physicochemical properties and environmental fate endpoints

    EPA Science Inventory

    Collecting the chemical structures and data for necessary QSAR modeling is facilitated by available public databases and open data. However, QSAR model performance is dependent on the quality of data and modeling methodology used. This study developed robust QSAR models for physi...

  19. Air Sensor Toolbox

    EPA Pesticide Factsheets

    Air Sensor Toolbox provides information to citizen scientists, researchers and developers interested in learning more about new lower-cost compact air sensor technologies and tools for measuring air quality.

  20. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modeling

    EPA Science Inventory

    Increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associat...

  1. Arc_Mat: a Matlab-based spatial data analysis toolbox

    NASA Astrophysics Data System (ADS)

    Liu, Xingjian; Lesage, James

    2010-03-01

    This article presents an overview of Arc_Mat, a Matlab-based spatial data analysis software package whose source code has been placed in the public domain. An earlier version of the Arc_Mat toolbox was developed to extract map polygon and database information from ESRI shapefiles and provide high quality mapping in the Matlab software environment. We discuss revisions to the toolbox that: utilize enhanced computing and graphing capabilities of more recent versions of Matlab, restructure the toolbox with object-oriented programming features, and provide more comprehensive functions for spatial data analysis. The Arc_Mat toolbox functionality includes basic choropleth mapping; exploratory spatial data analysis that provides exploratory views of spatial data through various graphs, for example, histogram, Moran scatterplot, three-dimensional scatterplot, density distribution plot, and parallel coordinate plots; and more formal spatial data modeling that draws on the extensive Spatial Econometrics Toolbox functions. A brief review of the design aspects of the revised Arc_Mat is described, and we provide some illustrative examples that highlight representative uses of the toolbox. Finally, we discuss programming with and customizing the Arc_Mat toolbox functionalities.

  2. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

    PubMed Central

    Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

    2009-01-01

    Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations

  3. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

    PubMed

    Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

    2009-07-16

    Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant

  4. DICOM router: an open source toolbox for communication and correction of DICOM objects.

    PubMed

    Hackländer, Thomas; Kleber, Klaus; Martin, Jens; Mertens, Heinrich

    2005-03-01

    Today, the exchange of medical images and clinical information is well defined by the digital imaging and communications in medicine (DICOM) and Health Level Seven (ie, HL7) standards. The interoperability among information systems is specified by the integration profiles of IHE (Integrating the Healthcare Enterprise). However, older imaging modalities frequently do not correctly support these interfaces and integration profiles, and some use cases are not yet specified by IHE. Therefore, corrections of DICOM objects are necessary to establish conformity. The aim of this project was to develop a toolbox that can automatically perform these recurrent corrections of the DICOM objects. The toolbox is composed of three main components: 1) a receiver to receive DICOM objects, 2) a processing pipeline to correct each object, and 3) one or more senders to forward each corrected object to predefined addressees. The toolbox is implemented under Java as an open source project. The processing pipeline is realized by means of plug ins. One of the plug ins can be programmed by the user via an external eXtensible Stylesheet Language (ie, XSL) file. Using this plug in, DICOM objects can also be converted into eXtensible Markup Language (ie, XML) documents or other data formats. DICOM storage services, DICOM CD-ROMs, and the local file system are defined as input and output channel. The toolbox is used clinically for different application areas. These are the automatic correction of DICOM objects from non-IHE-conforming modalities, the import of DICOM CD-ROMs into the picture archiving and communication system and the pseudo naming of DICOM images. The toolbox has been accepted by users in a clinical setting. Because of the open programming interfaces, the functionality can easily be adapted to future applications.

  5. Construction of multi-functional open modulized Matlab simulation toolbox for imaging ladar system

    NASA Astrophysics Data System (ADS)

    Wu, Long; Zhao, Yuan; Tang, Meng; He, Jiang; Zhang, Yong

    2011-06-01

    Ladar system simulation is to simulate the ladar models using computer simulation technology in order to predict the performance of the ladar system. This paper presents the developments of laser imaging radar simulation for domestic and overseas studies and the studies of computer simulation on ladar system with different application requests. The LadarSim and FOI-LadarSIM simulation facilities of Utah State University and Swedish Defence Research Agency are introduced in details. This paper presents the low level of simulation scale, un-unified design and applications of domestic researches in imaging ladar system simulation, which are mostly to achieve simple function simulation based on ranging equations for ladar systems. Design of laser imaging radar simulation with open and modularized structure is proposed to design unified modules for ladar system, laser emitter, atmosphere models, target models, signal receiver, parameters setting and system controller. Unified Matlab toolbox and standard control modules have been built with regulated input and output of the functions, and the communication protocols between hardware modules. A simulation based on ICCD gain-modulated imaging ladar system for a space shuttle is made based on the toolbox. The simulation result shows that the models and parameter settings of the Matlab toolbox are able to simulate the actual detection process precisely. The unified control module and pre-defined parameter settings simplify the simulation of imaging ladar detection. Its open structures enable the toolbox to be modified for specialized requests. The modulization gives simulations flexibility.

  6. Merging Applicability Domains for in Silico Assessment of Chemical Mutagenicity

    DTIC Science & Technology

    2014-02-04

    molecular fingerprints as descriptors for developing quantitative structure−activity relationship ( QSAR ) models and defining applicability domains with...used to define and quantify an applicability domain for either method. The importance of using applicability domains in QSAR modeling cannot be...domain from roughly 80% to 90%. These results indicated that the proposed QSAR protocol constituted a highly robust chemical mutagenicity prediction

  7. General baseline toxicity QSAR for nonpolar, polar and ionisable chemicals and their mixtures in the bioluminescence inhibition assay with Aliivibrio fischeri.

    PubMed

    Escher, Beate I; Baumer, Andreas; Bittermann, Kai; Henneberger, Luise; König, Maria; Kühnert, Christin; Klüver, Nils

    2017-03-22

    The Microtox assay, a bioluminescence inhibition assay with the marine bacterium Aliivibrio fischeri, is one of the most popular bioassays for assessing the cytotoxicity of organic chemicals, mixtures and environmental samples. Most environmental chemicals act as baseline toxicants in this short-term screening assay, which is typically run with only 30 min of exposure duration. Numerous Quantitative Structure-Activity Relationships (QSARs) exist for the Microtox assay for nonpolar and polar narcosis. However, typical water pollutants, which have highly diverse structures covering a wide range of hydrophobicity and speciation from neutral to anionic and cationic, are often outside the applicability domain of these QSARs. To include all types of environmentally relevant organic pollutants we developed a general baseline toxicity QSAR using liposome-water distribution ratios as descriptors. Previous limitations in availability of experimental liposome-water partition constants were overcome by reliable prediction models based on polyparameter linear free energy relationships for neutral chemicals and the COSMOmic model for charged chemicals. With this QSAR and targeted mixture experiments we could demonstrate that ionisable chemicals fall in the applicability domain. Most investigated water pollutants acted as baseline toxicants in this bioassay, with the few outliers identified as uncouplers or reactive toxicants. The main limitation of the Microtox assay is that chemicals with a high melting point and/or high hydrophobicity were outside of the applicability domain because of their low water solubility. We quantitatively derived a solubility cut-off but also demonstrated with mixture experiments that chemicals inactive on their own can contribute to mixture toxicity, which is highly relevant for complex environmental mixtures, where these chemicals may be present at concentrations below the solubility cut-off.

  8. Experimental design based 3-D QSAR analysis of steroid-protein interactions: Application to human CBG complexes

    NASA Astrophysics Data System (ADS)

    Norinder, Ulf

    1990-12-01

    An experimental design based 3-D QSAR analysis using a combination of principal component and PLS analysis is presented and applied to human corticosteroid-binding globulin complexes. The predictive capability of the created model is good. The technique can also be used as guidance when selecting new compounds to be investigated.

  9. 40 CFR 141.716 - Source toolbox components.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... for Microbial Toolbox Components § 141.716 Source toolbox components. (a) Watershed control program. Systems receive 0.5-log Cryptosporidium treatment credit for implementing a watershed control program that meets the requirements of this section. (1) Systems that intend to apply for the watershed control...

  10. 40 CFR 141.716 - Source toolbox components.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... for Microbial Toolbox Components § 141.716 Source toolbox components. (a) Watershed control program. Systems receive 0.5-log Cryptosporidium treatment credit for implementing a watershed control program that meets the requirements of this section. (1) Systems that intend to apply for the watershed control...

  11. 40 CFR 141.716 - Source toolbox components.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... for Microbial Toolbox Components § 141.716 Source toolbox components. (a) Watershed control program. Systems receive 0.5-log Cryptosporidium treatment credit for implementing a watershed control program that meets the requirements of this section. (1) Systems that intend to apply for the watershed control...

  12. An experimental toolbox for the generation of cold and ultracold polar molecules

    NASA Astrophysics Data System (ADS)

    Zeppenfeld, Martin; Gantner, Thomas; Glöckner, Rosa; Ibrügger, Martin; Koller, Manuel; Prehn, Alexander; Wu, Xing; Chervenkov, Sotir; Rempe, Gerhard

    2017-01-01

    Cold and ultracold molecules enable fascinating applications in quantum science. We present our toolbox of techniques to generate the required molecule ensembles, including buffergas cooling, centrifuge deceleration and optoelectrical Sisyphus cooling. We obtain excellent control over both the motional and internal molecular degrees of freedom, allowing us to aim at various applications.

  13. The importance of data curation on QSAR Modeling - PHYSPROP open data as a case study. (QSAR 2016)

    EPA Science Inventory

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These ...

  14. ICT: isotope correction toolbox.

    PubMed

    Jungreuthmayer, Christian; Neubauer, Stefan; Mairinger, Teresa; Zanghellini, Jürgen; Hann, Stephan

    2016-01-01

    Isotope tracer experiments are an invaluable technique to analyze and study the metabolism of biological systems. However, isotope labeling experiments are often affected by naturally abundant isotopes especially in cases where mass spectrometric methods make use of derivatization. The correction of these additive interferences--in particular for complex isotopic systems--is numerically challenging and still an emerging field of research. When positional information is generated via collision-induced dissociation, even more complex calculations for isotopic interference correction are necessary. So far, no freely available tools can handle tandem mass spectrometry data. We present isotope correction toolbox, a program that corrects tandem mass isotopomer data from tandem mass spectrometry experiments. Isotope correction toolbox is written in the multi-platform programming language Perl and, therefore, can be used on all commonly available computer platforms. Source code and documentation can be freely obtained under the Artistic License or the GNU General Public License from: https://github.com/jungreuc/isotope_correction_toolbox/ {christian.jungreuthmayer@boku.ac.at,juergen.zanghellini@boku.ac.at} Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  15. Rational selection of training and test sets for the development of validated QSAR models

    NASA Astrophysics Data System (ADS)

    Golbraikh, Alexander; Shen, Min; Xiao, Zhiyan; Xiao, Yun-De; Lee, Kuo-Hsiung; Tropsha, Alexander

    2003-02-01

    Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors ( kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R2 (q2) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q 2 for the training set and accuracy of prediction ( R 2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.

  16. A MODE-OF-ACTION-BASED QSAR APPROACH TO IMPROVE UNDERSTANDING OF DEVELOPMENTAL TOXICITY

    EPA Science Inventory

    QSAR models of developmental toxicity (devtox) have met with limited regulatory acceptance due to the use of ill-defined endpoints, lack of biological interpretability, and poor model performance. More generally, the lack of biological inference of many QSAR models is often due t...

  17. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. SCoT: a Python toolbox for EEG source connectivity.

    PubMed

    Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

    2014-01-01

    Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

  19. SCoT: a Python toolbox for EEG source connectivity

    PubMed Central

    Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

    2014-01-01

    Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

  20. Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

    NASA Astrophysics Data System (ADS)

    Santos-Filho, Osvaldo A.; Mishra, Rama K.; Hopfinger, A. J.

    2001-09-01

    Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target (`receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

  1. Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

    PubMed

    Du, Qi-Shi; Huang, Ri-Bo; Wei, Yu-Tuo; Pang, Zong-Wen; Du, Li-Qin; Chou, Kuo-Chen

    2009-01-30

    In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (c) 2008 Wiley Periodicals, Inc.

  2. Statistical molecular design of balanced compound libraries for QSAR modeling.

    PubMed

    Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

    2010-01-01

    A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

  3. QSAR modeling based on structure-information for properties of interest in human health.

    PubMed

    Hall, L H; Hall, L M

    2005-01-01

    The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.

  4. Structure–activity relationships study of mTOR kinase inhibition using QSAR and structure-based drug design approaches

    PubMed Central

    Lakhlili, Wiame; Yasri, Abdelaziz; Ibrahimi, Azeddine

    2016-01-01

    The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be a challenging task. The quantitative structure–activity relationship (QSAR) approach is a very useful and widespread technique for ligand-based drug design, which can be used to identify novel and potent mTOR inhibitors. In this study, we performed two-dimensional QSAR tests, and molecular docking validation tests of a series of mTOR ATP-competitive inhibitors to elucidate their structural properties associated with their activity. The QSAR tests were performed using partial least square method with a correlation coefficient of r2=0.799 and a cross-validation of q2=0.714. The chemical library screening was done by associating ligand-based to structure-based approach using the three-dimensional structure of mTOR developed by homology modeling. We were able to select 22 compounds from two databases as inhibitors of the mTOR kinase active site. We believe that the method and applications highlighted in this study will help future efforts toward the design of selective ATP-competitive inhibitors. PMID:27980424

  5. High Add Valued Application of Turpentine in Crop Production through Structural Modification and QSAR Analysis.

    PubMed

    Gao, Yanqing; Li, Jingjing; Li, Jian; Song, Zhanqian; Shang, Shibin; Rao, Xiaoping

    2018-02-08

    Turpentine is a volatile component of resin, which is an abundant forest resource in Southern China. As one of the most important components, the integrated application of β-pinene has been studied. The broad-spectrum evaluation of β-pinene and its analogues has, therefore, been necessary. In an attempt to expand the scope of agro-activity trials, the preparation and the evaluation of the herbicidal activity of a series of β-pinene analogues against three agricultural herbs were carried out. In accordance with the overall herbicidal activity, it is noteworthy that compounds 6k , 6l , and 6m demonstrated extreme activity with IC 50 values of 0.065, 0.065, and 0.052 mol active ingredients/hectare against E. crus-galli . The preliminary structure-activity relationship (SAR) was analyzed and the compounds with the appropriate volatility and substituent type that had beneficial herbicidal activity were analyzed. Simultaneously, the quantitative structure-activity relationship (QSAR) model was built and the most important structural features were indicated, which was, to a certain extent, in line with the SAR study. The study aimed to study the application of the forest resource turpentine in agriculture as a potential and alternative approach for comprehensive utilization.

  6. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

    PubMed

    Winkler, David A; Le, Tu C

    2017-01-01

    Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. A toolbox and a record for scientific model development

    NASA Technical Reports Server (NTRS)

    Ellman, Thomas

    1994-01-01

    Scientific computation can benefit from software tools that facilitate construction of computational models, control the application of models, and aid in revising models to handle new situations. Existing environments for scientific programming provide only limited means of handling these tasks. This paper describes a two pronged approach for handling these tasks: (1) designing a 'Model Development Toolbox' that includes a basic set of model constructing operations; and (2) designing a 'Model Development Record' that is automatically generated during model construction. The record is subsequently exploited by tools that control the application of scientific models and revise models to handle new situations. Our two pronged approach is motivated by our belief that the model development toolbox and record should be highly interdependent. In particular, a suitable model development record can be constructed only when models are developed using a well defined set of operations. We expect this research to facilitate rapid development of new scientific computational models, to help ensure appropriate use of such models and to facilitate sharing of such models among working computational scientists. We are testing this approach by extending SIGMA, and existing knowledge-based scientific software design tool.

  8. Turbo-Satori: a neurofeedback and brain-computer interface toolbox for real-time functional near-infrared spectroscopy.

    PubMed

    Lührs, Michael; Goebel, Rainer

    2017-10-01

    Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications. It communicates directly with common NIRx fNIRS hardware and was tested extensively ensuring that the calculations can be performed in real time without a significant change in calculation times for all sampling intervals during ongoing experiments of up to 6 h of recording. Enabling immediate access to advanced processing features also allows the use of this toolbox for students and nonexperts in the field of fNIRS data acquisition and processing. Flexible network interfaces allow third party stimulus applications to access the processed data and calculated statistics in real time so that this information can be easily incorporated in neurofeedback or BCI presentations.

  9. PFA toolbox: a MATLAB tool for Metabolic Flux Analysis.

    PubMed

    Morales, Yeimy; Bosque, Gabriel; Vehí, Josep; Picó, Jesús; Llaneras, Francisco

    2016-07-11

    Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User's Guide with a thorough description of its functions and several examples. The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.

  10. Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

    PubMed Central

    Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

    2015-01-01

    Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

  11. OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

    EPA Science Inventory

    The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

  12. 3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM.

    PubMed

    Ghafouri, Hamidreza; Ranjbar, Mohsen; Sakhteman, Amirhossein

    2017-08-01

    A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q 2 LOO-CV =1, R 2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Emotion assessment using the NIH Toolbox

    PubMed Central

    Butt, Zeeshan; Pilkonis, Paul A.; Cyranowski, Jill M.; Zill, Nicholas; Hendrie, Hugh C.; Kupst, Mary Jo; Kelly, Morgen A. R.; Bode, Rita K.; Choi, Seung W.; Lai, Jin-Shei; Griffith, James W.; Stoney, Catherine M.; Brouwers, Pim; Knox, Sarah S.; Cella, David

    2013-01-01

    One of the goals of the NIH Toolbox for Assessment of Neurological and Behavioral Function was to identify or develop brief measures of emotion for use in prospective epidemiologic and clinical research. Emotional health has significant links to physical health and exerts a powerful effect on perceptions of life quality. Based on an extensive literature review and expert input, the Emotion team identified 4 central subdomains: Negative Affect, Psychological Well-Being, Stress and Self-Efficacy, and Social Relationships. A subsequent psychometric review identified several existing self-report and proxy measures of these subdomains with measurement characteristics that met the NIH Toolbox criteria. In cases where adequate measures did not exist, robust item banks were developed to assess concepts of interest. A population-weighted sample was recruited by an online survey panel to provide initial item calibration and measure validation data. Participants aged 8 to 85 years completed self-report measures whereas parents/guardians responded for children aged 3 to 12 years. Data were analyzed using a combination of classic test theory and item response theory methods, yielding efficient measures of emotional health concepts. An overview of the development of the NIH Toolbox Emotion battery is presented along with preliminary results. Norming activities led to further refinement of the battery, thus enhancing the robustness of emotional health measurement for researchers using the NIH Toolbox. PMID:23479549

  14. The importance of data curation on QSAR Modeling ...

    EPA Pesticide Factsheets

    During the last few decades many QSAR models and tools have been developed at the US EPA, including the widely used EPISuite. During this period the arsenal of computational capabilities supporting cheminformatics has broadened dramatically with multiple software packages. These modern tools allow for more advanced techniques in terms of chemical structure representation and storage, as well as enabling automated data-mining and standardization approaches to examine and fix data quality issues.This presentation will investigate the impact of data curation on the reliability of QSAR models being developed within the EPA‘s National Center for Computational Toxicology. As part of this work we have attempted to disentangle the influence of the quality versus quantity of data based on the Syracuse PHYSPROP database partly used by EPISuite software. We will review our automated approaches to examining key datasets related to the EPISuite data to validate across chemical structure representations (e.g., mol file and SMILES) and identifiers (chemical names and registry numbers) and approaches to standardize data into QSAR-ready formats prior to modeling procedures. Our efforts to quantify and segregate data into quality categories has allowed us to evaluate the resulting models that can be developed from these data slices and to quantify to what extent efforts developing high-quality datasets have the expected pay-off in terms of predicting performance. The most accur

  15. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

    PubMed Central

    Song, Fucheng; Zhang, Anling; Liang, Hui; Cui, Lianhua; Li, Wenlian; Si, Hongzong; Duan, Yunbo; Zhai, Honglin

    2016-01-01

    A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method. PMID:27854309

  16. 3D-QSAR and molecular docking studies on HIV protease inhibitors

    NASA Astrophysics Data System (ADS)

    Tong, Jianbo; Wu, Yingji; Bai, Min; Zhan, Pei

    2017-02-01

    In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of 0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

  17. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

    PubMed

    Castillo-Garit, Juan Alberto; Abad, Concepción; Rodríguez-Borges, J Enrique; Marrero-Ponce, Yovani; Torrens, Francisco

    2012-01-01

    The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

  18. Velocity Mapping Toolbox (VMT): a processing and visualization suite for moving-vessel ADCP measurements

    USGS Publications Warehouse

    Parsons, D.R.; Jackson, P.R.; Czuba, J.A.; Engel, F.L.; Rhoads, B.L.; Oberg, K.A.; Best, J.L.; Mueller, D.S.; Johnson, K.K.; Riley, J.D.

    2013-01-01

    The use of acoustic Doppler current profilers (ADCP) for discharge measurements and three-dimensional flow mapping has increased rapidly in recent years and has been primarily driven by advances in acoustic technology and signal processing. Recent research has developed a variety of methods for processing data obtained from a range of ADCP deployments and this paper builds on this progress by describing new software for processing and visualizing ADCP data collected along transects in rivers or other bodies of water. The new utility, the Velocity Mapping Toolbox (VMT), allows rapid processing (vector rotation, projection, averaging and smoothing), visualization (planform and cross-section vector and contouring), and analysis of a range of ADCP-derived datasets. The paper documents the data processing routines in the toolbox and presents a set of diverse examples that demonstrate its capabilities. The toolbox is applicable to the analysis of ADCP data collected in a wide range of aquatic environments and is made available as open-source code along with this publication.

  19. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

    PubMed

    Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

    2016-06-01

    Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. QSAR Modeling and Prediction of Drug-Drug Interactions.

    PubMed

    Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

    2016-02-01

    Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

  1. Motor assessment using the NIH Toolbox

    PubMed Central

    Magasi, Susan; McCreath, Heather E.; Bohannon, Richard W.; Wang, Ying-Chih; Bubela, Deborah J.; Rymer, William Z.; Beaumont, Jennifer; Rine, Rose Marie; Lai, Jin-Shei; Gershon, Richard C.

    2013-01-01

    Motor function involves complex physiologic processes and requires the integration of multiple systems, including neuromuscular, musculoskeletal, and cardiopulmonary, and neural motor and sensory-perceptual systems. Motor-functional status is indicative of current physical health status, burden of disease, and long-term health outcomes, and is integrally related to daily functioning and quality of life. Given its importance to overall neurologic health and function, motor function was identified as a key domain for inclusion in the NIH Toolbox for Assessment of Neurological and Behavioral Function (NIH Toolbox). We engaged in a 3-stage developmental process to: 1) identify key subdomains and candidate measures for inclusion in the NIH Toolbox, 2) pretest candidate measures for feasibility across the age span of people aged 3 to 85 years, and 3) validate candidate measures against criterion measures in a sample of healthy individuals aged 3 to 85 years (n = 340). Based on extensive literature review and input from content experts, the 5 subdomains of dexterity, strength, balance, locomotion, and endurance were recommended for inclusion in the NIH Toolbox motor battery. Based on our validation testing, valid and reliable measures that are simultaneously low-cost and portable have been recommended to assess each subdomain, including the 9-hole peg board for dexterity, grip dynamometry for upper-extremity strength, standing balance test, 4-m walk test for gait speed, and a 2-minute walk test for endurance. PMID:23479547

  2. Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis

    PubMed Central

    Putz, Mihai V.; Ionaşcu, Cosmin; Putz, Ana-Maria; Ostafe, Vasile

    2011-01-01

    Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,…)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A−ASA,X1SA,X2SA,…). We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. PMID:21954348

  3. SOCIB Glider toolbox: from sensor to data repository

    NASA Astrophysics Data System (ADS)

    Pau Beltran, Joan; Heslop, Emma; Ruiz, Simón; Troupin, Charles; Tintoré, Joaquín

    2015-04-01

    Nowadays in oceanography, gliders constitutes a mature, cost-effective technology for the acquisition of measurements independently of the sea state (unlike ships), providing subsurface data during sustained periods, including extreme weather events. The SOCIB glider toolbox is a set of MATLAB/Octave scripts and functions developed in order to manage the data collected by a glider fleet. They cover the main stages of the data management process, both in real-time and delayed-time modes: metadata aggregation, downloading, processing, and automatic generation of data products and figures. The toolbox is distributed under the GNU licence (http://www.gnu.org/copyleft/gpl.html) and is available at http://www.socib.es/users/glider/glider_toolbox.

  4. Image processing and pattern recognition with CVIPtools MATLAB toolbox: automatic creation of masks for veterinary thermographic images

    NASA Astrophysics Data System (ADS)

    Mishra, Deependra K.; Umbaugh, Scott E.; Lama, Norsang; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

    2016-09-01

    CVIPtools is a software package for the exploration of computer vision and image processing developed in the Computer Vision and Image Processing Laboratory at Southern Illinois University Edwardsville. CVIPtools is available in three variants - a) CVIPtools Graphical User Interface, b) CVIPtools C library and c) CVIPtools MATLAB toolbox, which makes it accessible to a variety of different users. It offers students, faculty, researchers and any user a free and easy way to explore computer vision and image processing techniques. Many functions have been implemented and are updated on a regular basis, the library has reached a level of sophistication that makes it suitable for both educational and research purposes. In this paper, the detail list of the functions available in the CVIPtools MATLAB toolbox are presented and how these functions can be used in image analysis and computer vision applications. The CVIPtools MATLAB toolbox allows the user to gain practical experience to better understand underlying theoretical problems in image processing and pattern recognition. As an example application, the algorithm for the automatic creation of masks for veterinary thermographic images is presented.

  5. Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method

    PubMed Central

    2012-01-01

    Background The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. Results In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have

  6. Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.

    PubMed

    Gao, Jun; Che, Dongsheng; Zheng, Vincent W; Zhu, Ruixin; Liu, Qi

    2012-07-31

    The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway. In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several

  7. Validity and validation of expert (Q)SAR systems.

    PubMed

    Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

    2005-08-01

    At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

  8. Orbit Determination Toolbox

    NASA Technical Reports Server (NTRS)

    Carpenter, James R.; Berry, Kevin; Gregpru. Late; Speckman, Keith; Hur-Diaz, Sun; Surka, Derek; Gaylor, Dave

    2010-01-01

    The Orbit Determination Toolbox is an orbit determination (OD) analysis tool based on MATLAB and Java that provides a flexible way to do early mission analysis. The toolbox is primarily intended for advanced mission analysis such as might be performed in concept exploration, proposal, early design phase, or rapid design center environments. The emphasis is on flexibility, but it has enough fidelity to produce credible results. Insight into all flight dynamics source code is provided. MATLAB is the primary user interface and is used for piecing together measurement and dynamic models. The Java Astrodynamics Toolbox is used as an engine for things that might be slow or inefficient in MATLAB, such as high-fidelity trajectory propagation, lunar and planetary ephemeris look-ups, precession, nutation, polar motion calculations, ephemeris file parsing, and the like. The primary analysis functions are sequential filter/smoother and batch least-squares commands that incorporate Monte-Carlo data simulation, linear covariance analysis, measurement processing, and plotting capabilities at the generic level. These functions have a user interface that is based on that of the MATLAB ODE suite. To perform a specific analysis, users write MATLAB functions that implement truth and design system models. The user provides his or her models as inputs to the filter commands. The software provides a capability to publish and subscribe to a software bus that is compliant with the NASA Goddard Mission Services Evolution Center (GMSEC) standards, to exchange data with other flight dynamics tools to simplify the flight dynamics design cycle. Using the publish and subscribe approach allows for analysts in a rapid design center environment to seamlessly incorporate changes in spacecraft and mission design into navigation analysis and vice versa.

  9. Screening and assessment of chronic pain among children with cerebral palsy: a process evaluation of a pain toolbox.

    PubMed

    Orava, Taryn; Provvidenza, Christine; Townley, Ashleigh; Kingsnorth, Shauna

    2018-06-08

    Though high numbers of children with cerebral palsy experience chronic pain, it remains under-recognized. This paper describes an evaluation of implementation supports and adoption of the Chronic Pain Assessment Toolbox for Children with Disabilities (the Toolbox) to enhance pain screening and assessment practices within a pediatric rehabilitation and complex continuing care hospital. A multicomponent knowledge translation strategy facilitated Toolbox adoption, inclusive of a clinical practice guideline, cerebral palsy practice points and assessment tools. Across the hospital, seven ambulatory care clinics with cerebral palsy caseloads participated in a staggered roll-out (Group 1: exclusive CP caseloads, March-December; Group 2: mixed diagnostic caseloads, August-December). Evaluation measures included client electronic medical record audit, document review and healthcare provider survey and interviews. A significant change in documentation of pain screening and assessment practice from pre-Toolbox (<2%) to post-Toolbox adoption (53%) was found. Uptake in Group 2 clinics lagged behind Group 1. Opportunities to use the Toolbox consistently (based on diagnostic caseload) and frequently (based on client appointments) were noted among contextual factors identified. Overall, the Toolbox was positively received and clinically useful. Findings affirm that the Toolbox, in conjunction with the application of integrated knowledge translation principles and an established knowledge translation framework, has potential to be a useful resource to enrich and standardize chronic pain screening and assessment practices among children with cerebral palsy. Implications for Rehabilitation It is important to engage healthcare providers in the conceptualization, development, implementation and evaluation of a knowledge-to-action best practice product. The Chronic Pain Toolbox for Children with Disabilities provides rehabilitation staff with guidance on pain screening and assessment

  10. ObsPy: A Python Toolbox for Seismology - Recent Developments and Applications

    NASA Astrophysics Data System (ADS)

    Megies, T.; Krischer, L.; Barsch, R.; Sales de Andrade, E.; Beyreuther, M.

    2014-12-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project dedicated to building a bridge for seismology into the scientific Python ecosystem. It offersa) read and write support for essentially all commonly used waveform, station, and event metadata file formats with a unified interface,b) a comprehensive signal processing toolbox tuned to the needs of seismologists,c) integrated access to all large data centers, web services and databases, andd) convenient wrappers to legacy codes like libtau and evalresp.Python, currently the most popular language for teaching introductory computer science courses at top-ranked U.S. departments, is a full-blown programming language with the flexibility of an interactive scripting language. Its extensive standard library and large variety of freely available high quality scientific modules cover most needs in developing scientific processing workflows. Together with packages like NumPy, SciPy, Matplotlib, IPython, Pandas, lxml, and PyQt, ObsPy enables the construction of complete workflows in Python. These vary from reading locally stored data or requesting data from one or more different data centers through to signal analysis and data processing and on to visualizations in GUI and web applications, output of modified/derived data and the creation of publication-quality figures.ObsPy enjoys a large world-wide rate of adoption in the community. Applications successfully using it include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. All functionality is extensively documented and the ObsPy tutorial and gallery give a good impression of the wide range of possible use cases.We will present the basic features of ObsPy, new developments and applications, and a roadmap for the near future and discuss the sustainability of our open-source development model.

  11. EPA EMERGENCY PLANNING TOOLBOX

    EPA Science Inventory

    EPA's Office of Research and Development and Office of Water/Water Security Division have jointly developed a Response Protocol Toolbox (RPTB) to address the complex, multi-faceted challenges of a water utility's planning and response to intentional contamination of drinking wate...

  12. SEDIMENT-ASSOCIATED REACTIONS OF AROMATIC AMINES: QSAR DEVELOPMENT

    EPA Science Inventory

    Despite the common occurrence of the aromatic amine functional group in environmental contaminants, few quantitative structure-activity relationships (QSARs) have been developed to predict sorption kinetics for aromatic amines in natural soils and sediments. Towards the goal of d...

  13. Using Toxicological Evidence from QSAR Models in Practice

    EPA Science Inventory

    The new generation of QSAR models provides supporting documentation in addition to the predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances and to review and increase the reliability of toxicity predictions. Thi...

  14. A part toolbox to tune genetic expression in Bacillus subtilis

    PubMed Central

    Guiziou, Sarah; Sauveplane, Vincent; Chang, Hung-Ju; Clerté, Caroline; Declerck, Nathalie; Jules, Matthieu; Bonnet, Jerome

    2016-01-01

    Libraries of well-characterised components regulating gene expression levels are essential to many synthetic biology applications. While widely available for the Gram-negative model bacterium Escherichia coli, such libraries are lacking for the Gram-positive model Bacillus subtilis, a key organism for basic research and biotechnological applications. Here, we engineered a genetic toolbox comprising libraries of promoters, Ribosome Binding Sites (RBS), and protein degradation tags to precisely tune gene expression in B. subtilis. We first designed a modular Expression Operating Unit (EOU) facilitating parts assembly and modifications and providing a standard genetic context for gene circuits implementation. We then selected native, constitutive promoters of B. subtilis and efficient RBS sequences from which we engineered three promoters and three RBS sequence libraries exhibiting ∼14 000-fold dynamic range in gene expression levels. We also designed a collection of SsrA proteolysis tags of variable strength. Finally, by using fluorescence fluctuation methods coupled with two-photon microscopy, we quantified the absolute concentration of GFP in a subset of strains from the library. Our complete promoters and RBS sequences library comprising over 135 constructs enables tuning of GFP concentration over five orders of magnitude, from 0.05 to 700 μM. This toolbox of regulatory components will support many research and engineering applications in B. subtilis. PMID:27402159

  15. Watershed Modeling Applications with the Open-Access Modular Distributed Watershed Educational Toolbox (MOD-WET) and Introductory Hydrology Textbook

    NASA Astrophysics Data System (ADS)

    Huning, L. S.; Margulis, S. A.

    2014-12-01

    Traditionally, introductory hydrology courses focus on hydrologic processes as independent or semi-independent concepts that are ultimately integrated into a watershed model near the end of the term. When an "off-the-shelf" watershed model is introduced in the curriculum, this approach can result in a potential disconnect between process-based hydrology and the inherent interconnectivity of processes within the water cycle. In order to curb this and reduce the learning curve associated with applying hydrologic concepts to complex real-world problems, we developed the open-access Modular Distributed Watershed Educational Toolbox (MOD-WET). The user-friendly, MATLAB-based toolbox contains the same physical equations for hydrological processes (i.e. precipitation, snow, radiation, evaporation, unsaturated flow, infiltration, groundwater, and runoff) that are presented in the companion e-textbook (http://aqua.seas.ucla.edu/margulis_intro_to_hydro_textbook.html) and taught in the classroom. The modular toolbox functions can be used by students to study individual hydrologic processes. These functions are integrated together to form a simple spatially-distributed watershed model, which reinforces a holistic understanding of how hydrologic processes are interconnected and modeled. Therefore when watershed modeling is introduced, students are already familiar with the fundamental building blocks that have been unified in the MOD-WET model. Extensive effort has been placed on the development of a highly modular and well-documented code that can be run on a personal computer within the commonly-used MATLAB environment. MOD-WET was designed to: 1) increase the qualitative and quantitative understanding of hydrological processes at the basin-scale and demonstrate how they vary with watershed properties, 2) emphasize applications of hydrologic concepts rather than computer programming, 3) elucidate the underlying physical processes that can often be obscured with a complicated

  16. Wave data processing toolbox manual

    USGS Publications Warehouse

    Sullivan, Charlene M.; Warner, John C.; Martini, Marinna A.; Lightsom, Frances S.; Voulgaris, George; Work, Paul

    2006-01-01

    Researchers routinely deploy oceanographic equipment in estuaries, coastal nearshore environments, and shelf settings. These deployments usually include tripod-mounted instruments to measure a suite of physical parameters such as currents, waves, and pressure. Instruments such as the RD Instruments Acoustic Doppler Current Profiler (ADCP(tm)), the Sontek Argonaut, and the Nortek Aquadopp(tm) Profiler (AP) can measure these parameters. The data from these instruments must be processed using proprietary software unique to each instrument to convert measurements to real physical values. These processed files are then available for dissemination and scientific evaluation. For example, the proprietary processing program used to process data from the RD Instruments ADCP for wave information is called WavesMon. Depending on the length of the deployment, WavesMon will typically produce thousands of processed data files. These files are difficult to archive and further analysis of the data becomes cumbersome. More imperative is that these files alone do not include sufficient information pertinent to that deployment (metadata), which could hinder future scientific interpretation. This open-file report describes a toolbox developed to compile, archive, and disseminate the processed wave measurement data from an RD Instruments ADCP, a Sontek Argonaut, or a Nortek AP. This toolbox will be referred to as the Wave Data Processing Toolbox. The Wave Data Processing Toolbox congregates the processed files output from the proprietary software into two NetCDF files: one file contains the statistics of the burst data and the other file contains the raw burst data (additional details described below). One important advantage of this toolbox is that it converts the data into NetCDF format. Data in NetCDF format is easy to disseminate, is portable to any computer platform, and is viewable with public-domain freely-available software. Another important advantage is that a metadata

  17. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test.

    PubMed

    Klüver, Nils; Vogs, Carolina; Altenburger, Rolf; Escher, Beate I; Scholz, Stefan

    2016-12-01

    Fish embryos have become a popular model in ecotoxicology and toxicology. The fish embryo acute toxicity test (FET) with the zebrafish embryo was recently adopted by the OECD as technical guideline TG 236 and a large database of concentrations causing 50% lethality (LC 50 ) is available in the literature. Quantitative Structure-Activity Relationships (QSARs) of baseline toxicity (also called narcosis) are helpful to estimate the minimum toxicity of chemicals to be tested and to identify excess toxicity in existing data sets. Here, we analyzed an existing fish embryo toxicity database and established a QSAR for fish embryo LC 50 using chemicals that were independently classified to act according to the non-specific mode of action of baseline toxicity. The octanol-water partition coefficient K ow is commonly applied to discriminate between non-polar and polar narcotics. Replacing the K ow by the liposome-water partition coefficient K lipw yielded a common QSAR for polar and non-polar baseline toxicants. This developed baseline toxicity QSAR was applied to compare the final mode of action (MOA) assignment of 132 chemicals. Further, we included the analysis of internal lethal concentration (ILC 50 ) and chemical activity (La 50 ) as complementary approaches to evaluate the robustness of the FET baseline toxicity. The analysis of the FET dataset revealed that specifically acting and reactive chemicals converged towards the baseline toxicity QSAR with increasing hydrophobicity. The developed FET baseline toxicity QSAR can be used to identify specifically acting or reactive compounds by determination of the toxic ratio and in combination with appropriate endpoints to infer the MOA for chemicals. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Are the Chemical Structures in your QSAR Correct?

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumptio...

  19. Explaining Society: An Expanded Toolbox for Social Scientists

    PubMed Central

    Bell, David C.; Atkinson-Schnell, Jodie L.; DiBacco, Aron E.

    2012-01-01

    We propose for social scientists a theoretical toolbox containing a set of motivations that neurobiologists have recently validated. We show how these motivations can be used to create a theory of society recognizably similar to existing stable societies (sustainable, self-reproducing, and largely peaceful). Using this toolbox, we describe society in terms of three institutions: economy (a source of sustainability), government (peace), and the family (reproducibility). Conducting a thought experiment in three parts, we begin with a simple theory with only two motivations. We then create successive theories that systematically add motivations, showing that each element in the toolbox makes its own contribution to explain the workings of a stable society and that the family has a critical role in this process. PMID:23082093

  20. Aerospace Toolbox--a flight vehicle design, analysis, simulation, and software development environment II: an in-depth overview

    NASA Astrophysics Data System (ADS)

    Christian, Paul M.

    2002-07-01

    This paper presents a demonstrated approach to significantly reduce the cost and schedule of non real-time modeling and simulation, real-time HWIL simulation, and embedded code development. The tool and the methodology presented capitalize on a paradigm that has become a standard operating procedure in the automotive industry. The tool described is known as the Aerospace Toolbox, and it is based on the MathWorks Matlab/Simulink framework, which is a COTS application. Extrapolation of automotive industry data and initial applications in the aerospace industry show that the use of the Aerospace Toolbox can make significant contributions in the quest by NASA and other government agencies to meet aggressive cost reduction goals in development programs. The part I of this paper provided a detailed description of the GUI based Aerospace Toolbox and how it is used in every step of a development program; from quick prototyping of concept developments that leverage built-in point of departure simulations through to detailed design, analysis, and testing. Some of the attributes addressed included its versatility in modeling 3 to 6 degrees of freedom, its library of flight test validated library of models (including physics, environments, hardware, and error sources), and its built-in Monte Carlo capability. Other topics that were covered in part I included flight vehicle models and algorithms, and the covariance analysis package, Navigation System Covariance Analysis Tools (NavSCAT). Part II of this series will cover a more in-depth look at the analysis and simulation capability and provide an update on the toolbox enhancements. It will also address how the Toolbox can be used as a design hub for Internet based collaborative engineering tools such as NASA's Intelligent Synthesis Environment (ISE) and Lockheed Martin's Interactive Missile Design Environment (IMD).

  1. Aerospace Toolbox---a flight vehicle design, analysis, simulation ,and software development environment: I. An introduction and tutorial

    NASA Astrophysics Data System (ADS)

    Christian, Paul M.; Wells, Randy

    2001-09-01

    This paper presents a demonstrated approach to significantly reduce the cost and schedule of non real-time modeling and simulation, real-time HWIL simulation, and embedded code development. The tool and the methodology presented capitalize on a paradigm that has become a standard operating procedure in the automotive industry. The tool described is known as the Aerospace Toolbox, and it is based on the MathWorks Matlab/Simulink framework, which is a COTS application. Extrapolation of automotive industry data and initial applications in the aerospace industry show that the use of the Aerospace Toolbox can make significant contributions in the quest by NASA and other government agencies to meet aggressive cost reduction goals in development programs. The part I of this paper provides a detailed description of the GUI based Aerospace Toolbox and how it is used in every step of a development program; from quick prototyping of concept developments that leverage built-in point of departure simulations through to detailed design, analysis, and testing. Some of the attributes addressed include its versatility in modeling 3 to 6 degrees of freedom, its library of flight test validated library of models (including physics, environments, hardware, and error sources), and its built-in Monte Carlo capability. Other topics to be covered in this part include flight vehicle models and algorithms, and the covariance analysis package, Navigation System Covariance Analysis Tools (NavSCAT). Part II of this paper, to be published at a later date, will conclude with a description of how the Aerospace Toolbox is an integral part of developing embedded code directly from the simulation models by using the Mathworks Real Time Workshop and optimization tools. It will also address how the Toolbox can be used as a design hub for Internet based collaborative engineering tools such as NASA's Intelligent Synthesis Environment (ISE) and Lockheed Martin's Interactive Missile Design Environment

  2. Basic Radar Altimetry Toolbox: Tools to Use Radar Altimetry for Geodesy

    NASA Astrophysics Data System (ADS)

    Rosmorduc, V.; Benveniste, J. J.; Bronner, E.; Niejmeier, S.

    2010-12-01

    Radar altimetry is very much a technique expanding its applications and uses. If quite a lot of efforts have been made for oceanography users (including easy-to-use data), the use of those data for geodesy, especially combined witht ESA GOCE mission data is still somehow hard. ESA and CNES thus had the Basic Radar Altimetry Toolbox developed (as well as, on ESA side, the GOCE User Toolbox, both being linked). The Basic Radar Altimetry Toolbox is an "all-altimeter" collection of tools, tutorials and documents designed to facilitate the use of radar altimetry data. The software is able: - to read most distributed radar altimetry data, from ERS-1 & 2, Topex/Poseidon, Geosat Follow-on, Jason-1, Envisat, Jason- 2, CryoSat and the future Saral missions, - to perform some processing, data editing and statistic, - and to visualize the results. It can be used at several levels/several ways: - as a data reading tool, with APIs for C, Fortran, Matlab and IDL - as processing/extraction routines, through the on-line command mode - as an educational and a quick-look tool, with the graphical user interface As part of the Toolbox, a Radar Altimetry Tutorial gives general information about altimetry, the technique involved and its applications, as well as an overview of past, present and future missions, including information on how to access data and additional software and documentation. It also presents a series of data use cases, covering all uses of altimetry over ocean, cryosphere and land, showing the basic methods for some of the most frequent manners of using altimetry data. It is an opportunity to teach remote sensing with practical training. It has been available from April 2007, and had been demonstrated during training courses and scientific meetings. About 1200 people downloaded it (Summer 2010), with many "newcomers" to altimetry among them. Users' feedbacks, developments in altimetry, and practice, showed that new interesting features could be added. Some have been

  3. QSAR studies of macrocyclic diterpenes with P-glycoprotein inhibitory activity.

    PubMed

    Sousa, Inês J; Ferreira, Maria-José U; Molnár, Joseph; Fernandes, Miguel X

    2013-02-14

    Multidrug resistance (MDR) represents a major limitation for cancer chemotherapy. There are several mechanisms of MDR but the most important is associated with P-glycoprotein (P-gp) overexpression. The development of modulators of P-gp that are able to re-establish drug sensitivity of resistant cells has been considered a promising approach for overcoming MDR. Macrocyclic lathyrane and jatrophane-type diterpenes from Euphorbia species were found to be strong MDR reversing agents. In this study we applied quantitative structure-activity relationship (QSAR) methodology in order to identify the most relevant molecular features of macrocyclic diterpenes with P-gp inhibitory activity and to determine which structural modifications can be performed to improve their activity. Using experimental biological data at two concentrations (4 and 40 μg/ml), we developed a QSAR model for a set of 51 bioactive diterpenic compounds which includes lathyrane and jatrophane-type diterpenes and another model just for jatrophanes. The cross-validation correlation values for all diterpenes QSAR models developed for biological activities at compound concentrations of 4 and 40 μg/ml were 0.758 and 0.729, respectively. Regarding the prediction ability, we get R²(pred) values of 0.765 and 0.534 for biological activities at compound concentrations of 4 and 40 μg/ml, respectively. Applying the cross-validation test to jatrophanes QSAR models, we obtained 0.680 and 0.787 for biological activities at compound concentrations of 4 and 40 μg/ml concentrations, respectively. For the same concentrations, the obtained R²(pred) values for jatrophanes models were 0.541 and 0.534, respectively. The obtained models were statistically valid and showed high prediction ability. Copyright © 2012 Elsevier B.V. All rights reserved.

  4. National Water-Quality Assessment (NAWQA) Area-Characterization Toolbox

    USGS Publications Warehouse

    Price, Curtis

    2010-01-01

    This is release 1.0 of the National Water-Quality Assessment (NAWQA) Area-Characterization Toolbox. These tools are designed to be accessed using ArcGIS Desktop software (versions 9.3 and 9.3.1). The toolbox is composed of a collection of custom tools that implement geographic information system (GIS) techniques used by the NAWQA Program to characterize aquifer areas, drainage basins, and sampled wells.

  5. Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

    PubMed

    Lee, Yunho; von Gunten, Urs

    2012-12-01

    Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential.

    PubMed

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. CAMELOT: Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox

    NASA Astrophysics Data System (ADS)

    Di Carlo, Marilena; Romero Martin, Juan Manuel; Vasile, Massimiliano

    2018-03-01

    Computational-Analytical Multi-fidElity Low-thrust Optimisation Toolbox (CAMELOT) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. To do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made using two optimisation engines included in the toolbox, a single-objective global optimiser, and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of case studies: from the design of interplanetary trajectories to the optimal de-orbiting of space debris and from the deployment of constellations to on-orbit servicing. In this paper, the main elements of CAMELOT are described and two examples, solved using the toolbox, are presented.

  8. Evolution of the international workshops on quantitative structure-activity relationships (QSARs) in environmental toxicology.

    PubMed

    Kaiser, K L E

    2007-01-01

    This presentation will review the evolution of the workshops from a scientific and personal perspective. From their modest beginning in 1983, the workshops have developed into larger international meetings, regularly held every two years. Their initial focus on the aquatic sphere soon expanded to include properties and effects on atmospheric and terrestrial species, including man. Concurrent with this broadening of their scientific scope, the workshops have become an important forum for the early dissemination of all aspects of qualitative and quantitative structure-activity research in ecotoxicology and human health effects. Over the last few decades, the field of quantitative structure/activity relationships (QSARs) has quickly emerged as a major scientific method in understanding the properties and effects of chemicals on the environment and human health. From substances that only affect cell membranes to those that bind strongly to a specific enzyme, QSARs provides insight into the biological effects and chemical and physical properties of substances. QSARs are useful for delineating the quantitative changes in biological effects resulting from minor but systematic variations of the structure of a compound with a specific mode of action. In addition, more holistic approaches are being devised that result in our ability to predict the effects of structurally unrelated compounds with (potentially) different modes of action. Research in QSAR environmental toxicology has led to many improvements in the manufacturing, use, and disposal of chemicals. Furthermore, it has led to national policies and international agreements, from use restrictions or outright bans of compounds, such as polychlorinated biphenyls (PCBs), mirex, and highly chlorinated pesticides (e.g. DDT, dieldrin) for the protection of avian predators, to alternatives for ozone-depleting compounds, to better waste treatment systems, to more powerful and specific acting drugs. Most of the recent advances

  9. Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

    PubMed Central

    Scior, Thomas; Lozano-Aponte, Jorge; Ajmani, Subhash; Hernández-Montero, Eduardo; Chávez-Silva, Fabiola; Hernández-Núñez, Emanuel; Moo-Puc, Rosa; Fraguela-Collar, Andres; Navarrete-Vázquez, Gabriel

    2015-01-01

    In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target. PMID:25872791

  10. Reduced density gradient as a novel approach for estimating QSAR descriptors, and its application to 1, 4-dihydropyridine derivatives with potential antihypertensive effects.

    PubMed

    Jardínez, Christiaan; Vela, Alberto; Cruz-Borbolla, Julián; Alvarez-Mendez, Rodrigo J; Alvarado-Rodríguez, José G

    2016-12-01

    The relationship between the chemical structure and biological activity (log IC 50 ) of 40 derivatives of 1,4-dihydropyridines (DHPs) was studied using density functional theory (DFT) and multiple linear regression analysis methods. With the aim of improving the quantitative structure-activity relationship (QSAR) model, the reduced density gradient s( r) of the optimized equilibrium geometries was used as a descriptor to include weak non-covalent interactions. The QSAR model highlights the correlation between the log IC 50 with highest molecular orbital energy (E HOMO ), molecular volume (V), partition coefficient (log P), non-covalent interactions NCI(H4-G) and the dual descriptor [Δf(r)]. The model yielded values of R 2 =79.57 and Q 2 =69.67 that were validated with the next four internal analytical validations DK=0.076, DQ=-0.006, R P =0.056, and R N =0.000, and the external validation Q 2 boot =64.26. The QSAR model found can be used to estimate biological activity with high reliability in new compounds based on a DHP series. Graphical abstract The good correlation between the log IC 50 with the NCI (H4-G) estimated by the reduced density gradient approach of the DHP derivatives.

  11. ERP Reliability Analysis (ERA) Toolbox: An open-source toolbox for analyzing the reliability of event-related brain potentials.

    PubMed

    Clayson, Peter E; Miller, Gregory A

    2017-01-01

    Generalizability theory (G theory) provides a flexible, multifaceted approach to estimating score reliability. G theory's approach to estimating score reliability has important advantages over classical test theory that are relevant for research using event-related brain potentials (ERPs). For example, G theory does not require parallel forms (i.e., equal means, variances, and covariances), can handle unbalanced designs, and provides a single reliability estimate for designs with multiple sources of error. This monograph provides a detailed description of the conceptual framework of G theory using examples relevant to ERP researchers, presents the algorithms needed to estimate ERP score reliability, and provides a detailed walkthrough of newly-developed software, the ERP Reliability Analysis (ERA) Toolbox, that calculates score reliability using G theory. The ERA Toolbox is open-source, Matlab software that uses G theory to estimate the contribution of the number of trials retained for averaging, group, and/or event types on ERP score reliability. The toolbox facilitates the rigorous evaluation of psychometric properties of ERP scores recommended elsewhere in this special issue. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. FISSA: A neuropil decontamination toolbox for calcium imaging signals.

    PubMed

    Keemink, Sander W; Lowe, Scott C; Pakan, Janelle M P; Dylda, Evelyn; van Rossum, Mark C W; Rochefort, Nathalie L

    2018-02-22

    In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

  13. Improving student comprehension of the interconnectivity of the hydrologic cycle with a novel 'hydrology toolbox', integrated watershed model, and companion textbook

    NASA Astrophysics Data System (ADS)

    Huning, L. S.; Margulis, S. A.

    2013-12-01

    Concepts in introductory hydrology courses are often taught in the context of process-based modeling that ultimately is integrated into a watershed model. In an effort to reduce the learning curve associated with applying hydrologic concepts to real-world applications, we developed and incorporated a 'hydrology toolbox' that complements a new, companion textbook into introductory undergraduate hydrology courses. The hydrology toolbox contains the basic building blocks (functions coded in MATLAB) for an integrated spatially-distributed watershed model that makes hydrologic topics (e.g. precipitation, snow, radiation, evaporation, unsaturated flow, infiltration, groundwater, and runoff) more user-friendly and accessible for students. The toolbox functions can be used in a modular format so that students can study individual hydrologic processes and become familiar with the hydrology toolbox. This approach allows such courses to emphasize understanding and application of hydrologic concepts rather than computer coding or programming. While topics in introductory hydrology courses are often introduced and taught independently or semi-independently, they are inherently interconnected. These toolbox functions are therefore linked together at the end of the course to reinforce a holistic understanding of how these hydrologic processes are measured, interconnected, and modeled. They are integrated into a spatially-distributed watershed model or numerical laboratory where students can explore a range of topics such as rainfall-runoff modeling, urbanization, deforestation, watershed response to changes in parameters or forcings, etc. Model output can readily be visualized and analyzed by students to understand watershed response in a real river basin or a simple 'toy' basin. These tools complement the textbook, each of which has been well received by students in multiple hydrology courses with various disciplinary backgrounds. The same governing equations that students have

  14. Development of a CRISPR/Cas9 genome editing toolbox for Corynebacterium glutamicum.

    PubMed

    Liu, Jiao; Wang, Yu; Lu, Yujiao; Zheng, Ping; Sun, Jibin; Ma, Yanhe

    2017-11-16

    Corynebacterium glutamicum is an important industrial workhorse and advanced genetic engineering tools are urgently demanded. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) and their CRISPR-associated proteins (Cas) have revolutionized the field of genome engineering. The CRISPR/Cas9 system that utilizes NGG as protospacer adjacent motif (PAM) and has good targeting specificity can be developed into a powerful tool for efficient and precise genome editing of C. glutamicum. Herein, we developed a versatile CRISPR/Cas9 genome editing toolbox for C. glutamicum. Cas9 and gRNA expression cassettes were reconstituted to combat Cas9 toxicity and facilitate effective termination of gRNA transcription. Co-transformation of Cas9 and gRNA expression plasmids was exploited to overcome high-frequency mutation of cas9, allowing not only highly efficient gene deletion and insertion with plasmid-borne editing templates (efficiencies up to 60.0 and 62.5%, respectively) but also simple and time-saving operation. Furthermore, CRISPR/Cas9-mediated ssDNA recombineering was developed to precisely introduce small modifications and single-nucleotide changes into the genome of C. glutamicum with efficiencies over 80.0%. Notably, double-locus editing was also achieved in C. glutamicum. This toolbox works well in several C. glutamicum strains including the widely-used strains ATCC 13032 and ATCC 13869. In this study, we developed a CRISPR/Cas9 toolbox that could facilitate markerless gene deletion, gene insertion, precise base editing, and double-locus editing in C. glutamicum. The CRISPR/Cas9 toolbox holds promise for accelerating the engineering of C. glutamicum and advancing its application in the production of biochemicals and biofuels.

  15. Developing a congestion mitigation toolbox.

    DOT National Transportation Integrated Search

    2011-09-30

    Researchers created A Michigan Toolbox for Mitigating Traffic Congestion to be a useful desk reference for practitioners and an educational tool for elected officials acting through public policy boards to better understand the development, planning,...

  16. A Toolbox of Metrology-Based Techniques for Optical System Alignment

    NASA Technical Reports Server (NTRS)

    Coulter, Phillip; Ohl, Raymond G.; Blake, Peter N.; Bos, Brent J.; Casto, Gordon V.; Eichhorn, William L.; Gum, Jeffrey S.; Hadjimichael, Theodore J.; Hagopian, John G.; Hayden, Joseph E.; hide

    2016-01-01

    The NASA Goddard Space Flight Center (GSFC) and its partners have broad experience in the alignment of flight optical instruments and spacecraft structures. Over decades, GSFC developed alignment capabilities and techniques for a variety of optical and aerospace applications. In this paper, we provide an overview of a subset of the capabilities and techniques used on several recent projects in a toolbox format. We discuss a range of applications, from small-scale optical alignment of sensors to mirror and bench examples that make use of various large-volume metrology techniques. We also discuss instruments and analytical tools.

  17. A Toolbox of Metrology-Based Techniques for Optical System Alignment

    NASA Technical Reports Server (NTRS)

    Coulter, Phillip; Ohl, Raymond G.; Blake, Peter N.; Bos, Brent J.; Eichhorn, William L.; Gum, Jeffrey S.; Hadjimichael, Theodore J.; Hagopian, John G.; Hayden, Joseph E.; Hetherington, Samuel E.; hide

    2016-01-01

    The NASA Goddard Space Flight Center (GSFC) and its partners have broad experience in the alignment of flight optical instruments and spacecraft structures. Over decades, GSFC developed alignment capabilities and techniques for a variety of optical and aerospace applications. In this paper, we provide an overview of a subset of the capabilities and techniques used on several recent projects in a "toolbox" format. We discuss a range of applications, from small-scale optical alignment of sensors to mirror and bench examples that make use of various large-volume metrology techniques. We also discuss instruments and analytical tools.

  18. Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

    PubMed

    Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

    2002-01-01

    A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

  19. CBP TOOLBOX VERSION 2.0: CODE INTEGRATION ENHANCEMENTS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Smith, F.; Flach, G.; BROWN, K.

    2013-06-01

    This report describes enhancements made to code integration aspects of the Cementitious Barriers Project (CBP) Toolbox as a result of development work performed at the Savannah River National Laboratory (SRNL) in collaboration with Vanderbilt University (VU) in the first half of fiscal year 2013. Code integration refers to the interfacing to standalone CBP partner codes, used to analyze the performance of cementitious materials, with the CBP Software Toolbox. The most significant enhancements are: 1) Improved graphical display of model results. 2) Improved error analysis and reporting. 3) Increase in the default maximum model mesh size from 301 to 501 nodes.more » 4) The ability to set the LeachXS/Orchestra simulation times through the GoldSim interface. These code interface enhancements have been included in a new release (Version 2.0) of the CBP Toolbox.« less

  20. Smoke Ready Toolbox for Wildfires

    EPA Pesticide Factsheets

    This site provides an online Smoke Ready Toolbox for Wildfires, which lists resources and tools that provide information on health impacts from smoke exposure, current fire conditions and forecasts and strategies to reduce exposure to smoke.

  1. Development of a QSAR model for predicting aqueous reaction rate constants of organic chemicals with hydroxyl radicals.

    PubMed

    Luo, Xiang; Yang, Xianhai; Qiao, Xianliang; Wang, Ya; Chen, Jingwen; Wei, Xiaoxuan; Peijnenburg, Willie J G M

    2017-03-22

    Reaction with hydroxyl radicals (˙OH) is an important removal pathway for organic pollutants in the aquatic environment. The aqueous reaction rate constant (k OH ) is therefore an important parameter for fate assessment of aquatic pollutants. Since experimental determination fails to meet the requirement of being able to efficiently handle numerous organic chemicals at limited cost and within a relatively short period of time, in silico methods such as quantitative structure-activity relationship (QSAR) models are needed to predict k OH . In this study, a QSAR model with a larger and wider applicability domain as compared with existing models was developed. Following the guidelines for the development and validation of QSAR models proposed by the Organization for Economic Co-operation and Development (OECD), the model shows satisfactory performance. The applicability domain of the model has been extended and contained chemicals that have rarely been covered in most previous studies. The chemicals covered in the current model contain functional groups including [double bond splayed left]C[double bond, length as m-dash]C[double bond splayed right], -C[triple bond, length as m-dash]C-, -C 6 H 5 , -OH, -CHO, -O-, [double bond splayed left]C[double bond, length as m-dash]O, -C[double bond, length as m-dash]O(O)-, -COOH, -C[triple bond, length as m-dash]N, [double bond splayed left]N-, -NH 2 , -NH-C(O)-, -NO 2 , -N[double bond, length as m-dash]C-N[double bond splayed right], [double bond splayed left]N-N[double bond splayed right], -N[double bond, length as m-dash]N-, -S-, -S-S-, -SH, -SO 3 , -SO 4 , -PO 4 , and -X (F, Cl, Br, and I).

  2. 2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

    2017-09-01

    The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

  3. A Molecular Toolbox for Rapid Generation of Viral Vectors to Up- or Down-Regulate Neuronal Gene Expression in vivo

    PubMed Central

    White, Melanie D.; Milne, Ruth V. J.; Nolan, Matthew F.

    2011-01-01

    We introduce a molecular toolbox for manipulation of neuronal gene expression in vivo. The toolbox includes promoters, ion channels, optogenetic tools, fluorescent proteins, and intronic artificial microRNAs. The components are easily assembled into adeno-associated virus (AAV) or lentivirus vectors using recombination cloning. We demonstrate assembly of toolbox components into lentivirus and AAV vectors and use these vectors for in vivo expression of inwardly rectifying potassium channels (Kir2.1, Kir3.1, and Kir3.2) and an artificial microRNA targeted against the ion channel HCN1 (HCN1 miRNA). We show that AAV assembled to express HCN1 miRNA produces efficacious and specific in vivo knockdown of HCN1 channels. Comparison of in vivo viral transduction using HCN1 miRNA with mice containing a germ line deletion of HCN1 reveals similar physiological phenotypes in cerebellar Purkinje cells. The easy assembly and re-usability of the toolbox components, together with the ability to up- or down-regulate neuronal gene expression in vivo, may be useful for applications in many areas of neuroscience. PMID:21772812

  4. Expanding the KATE toolbox

    NASA Technical Reports Server (NTRS)

    Thomas, Stan J.

    1993-01-01

    KATE (Knowledge-based Autonomous Test Engineer) is a model-based software system developed in the Artificial Intelligence Laboratory at the Kennedy Space Center for monitoring, fault detection, and control of launch vehicles and ground support systems. In order to bring KATE to the level of performance, functionality, and integratability needed for firing room applications, efforts are underway to implement KATE in the C++ programming language using an X-windows interface. Two programs which were designed and added to the collection of tools which comprise the KATE toolbox are described. The first tool, called the schematic viewer, gives the KATE user the capability to view digitized schematic drawings in the KATE environment. The second tool, called the model editor, gives the KATE model builder a tool for creating and editing knowledge base files. Design and implementation issues having to do with these two tools are discussed. It will be useful to anyone maintaining or extending either the schematic viewer or the model editor.

  5. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    PubMed

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  6. EPA ExpoBox Toolbox Search

    EPA Pesticide Factsheets

    EPA ExpoBox is a toolbox for exposure assessors. Its purpose is to provide a compendium of exposure assessment and risk characterization tools that will present comprehensive step-by-step guidance and links to relevant assessment data bases,

  7. Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Valerio, Luis G.; Arvidson, Kirk B.; Chanderbhan, Ronald F.

    2007-07-01

    Consistent with the U.S. Food and Drug Administration (FDA) Critical Path Initiative, predictive toxicology software programs employing quantitative structure-activity relationship (QSAR) models are currently under evaluation for regulatory risk assessment and scientific decision support for highly sensitive endpoints such as carcinogenicity, mutagenicity and reproductive toxicity. At the FDA's Center for Food Safety and Applied Nutrition's Office of Food Additive Safety and the Center for Drug Evaluation and Research's Informatics and Computational Safety Analysis Staff (ICSAS), the use of computational SAR tools for both qualitative and quantitative risk assessment applications are being developed and evaluated. One tool of current interest ismore » MDL-QSAR predictive discriminant analysis modeling of rodent carcinogenicity, which has been previously evaluated for pharmaceutical applications by the FDA ICSAS. The study described in this paper aims to evaluate the utility of this software to estimate the carcinogenic potential of small, organic, naturally occurring chemicals found in the human diet. In addition, a group of 19 known synthetic dietary constituents that were positive in rodent carcinogenicity studies served as a control group. In the test group of naturally occurring chemicals, 101 were found to be suitable for predictive modeling using this software's discriminant analysis modeling approach. Predictions performed on these compounds were compared to published experimental evidence of each compound's carcinogenic potential. Experimental evidence included relevant toxicological studies such as rodent cancer bioassays, rodent anti-carcinogenicity studies, genotoxic studies, and the presence of chemical structural alerts. Statistical indices of predictive performance were calculated to assess the utility of the predictive modeling method. Results revealed good predictive performance using this software's rodent carcinogenicity module of over 1200

  8. RenderToolbox3: MATLAB tools that facilitate physically based stimulus rendering for vision research.

    PubMed

    Heasly, Benjamin S; Cottaris, Nicolas P; Lichtman, Daniel P; Xiao, Bei; Brainard, David H

    2014-02-07

    RenderToolbox3 provides MATLAB utilities and prescribes a workflow that should be useful to researchers who want to employ graphics in the study of vision and perhaps in other endeavors as well. In particular, RenderToolbox3 facilitates rendering scene families in which various scene attributes and renderer behaviors are manipulated parametrically, enables spectral specification of object reflectance and illuminant spectra, enables the use of physically based material specifications, helps validate renderer output, and converts renderer output to physical units of radiance. This paper describes the design and functionality of the toolbox and discusses several examples that demonstrate its use. We have designed RenderToolbox3 to be portable across computer hardware and operating systems and to be free and open source (except for MATLAB itself). RenderToolbox3 is available at https://github.com/DavidBrainard/RenderToolbox3.

  9. Toolbox for Renewable Energy Project Development

    EPA Pesticide Factsheets

    The Toolbox for Renewable Energy Project Development summarizes key project development issues, addresses how to overcome major hurdles, and provides a curated directory of project development resources.

  10. A Module for Graphical Display of Model Results with the CBP Toolbox

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Smith, F.

    2015-04-21

    This report describes work performed by the Savannah River National Laboratory (SRNL) in fiscal year 2014 to add enhanced graphical capabilities to display model results in the Cementitious Barriers Project (CBP) Toolbox. Because Version 2.0 of the CBP Toolbox has just been released, the graphing enhancements described in this report have not yet been integrated into a new version of the Toolbox. Instead they have been tested using a standalone GoldSim model and, while they are substantially complete, may undergo further refinement before full implementation. Nevertheless, this report is issued to document the FY14 development efforts which will provide amore » basis for further development of the CBP Toolbox.« less

  11. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies

    NASA Astrophysics Data System (ADS)

    Manoharan, Prabu; Vijayan, R. S. K.; Ghoshal, Nanda

    2010-10-01

    The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables ( X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

  12. Rationalizing fragment based drug discovery for BACE1: insights from FB-QSAR, FB-QSSR, multi objective (MO-QSPR) and MIF studies.

    PubMed

    Manoharan, Prabu; Vijayan, R S K; Ghoshal, Nanda

    2010-10-01

    The ability to identify fragments that interact with a biological target is a key step in FBDD. To date, the concept of fragment based drug design (FBDD) is increasingly driven by bio-physical methods. To expand the boundaries of QSAR paradigm, and to rationalize FBDD using In silico approach, we propose a fragment based QSAR methodology referred here in as FB-QSAR. The FB-QSAR methodology was validated on a dataset consisting of 52 Hydroxy ethylamine (HEA) inhibitors, disclosed by GlaxoSmithKline Pharmaceuticals as potential anti-Alzheimer agents. To address the issue of target selectivity, a major confounding factor in the development of selective BACE1 inhibitors, FB-QSSR models were developed using the reported off target activity values. A heat map constructed, based on the activity and selectivity profile of the individual R-group fragments, and was in turn used to identify superior R-group fragments. Further, simultaneous optimization of multiple properties, an issue encountered in real-world drug discovery scenario, and often overlooked in QSAR approaches, was addressed using a Multi Objective (MO-QSPR) method that balances properties, based on the defined objectives. MO-QSPR was implemented using Derringer and Suich desirability algorithm to identify the optimal level of independent variables (X) that could confer a trade-off between selectivity and activity. The results obtained from FB-QSAR were further substantiated using MIF (Molecular Interaction Fields) studies. To exemplify the potentials of FB-QSAR and MO-QSPR in a pragmatic fashion, the insights gleaned from the MO-QSPR study was reverse engineered using Inverse-QSAR in a combinatorial fashion to enumerate some prospective novel, potent and selective BACE1 inhibitors.

  13. The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study.

    PubMed

    Golubović, Mlađan; Lazarević, Milan; Zlatanović, Dragan; Krtinić, Dane; Stoičkov, Viktor; Mladenović, Bojan; Milić, Dragan J; Sokolović, Dušan; Veselinović, Aleksandar M

    2018-04-13

    Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

    PubMed

    Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

    2009-01-15

    This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

  15. Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

    PubMed

    Martínez-Santiago, O; Marrero-Ponce, Y; Vivas-Reyes, R; Rivera-Borroto, O M; Hurtado, E; Treto-Suarez, M A; Ramos, Y; Vergara-Murillo, F; Orozco-Ugarriza, M E; Martínez-López, Y

    2017-05-01

    Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively

  16. Sparse QSAR modelling methods for therapeutic and regenerative medicine

    NASA Astrophysics Data System (ADS)

    Winkler, David A.

    2018-02-01

    The quantitative structure-activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.

  17. A toolbox for computing pebble shape and roundness indexes: experimental tests and recommendations for future applications.

    NASA Astrophysics Data System (ADS)

    Cassel, M.; Piegay, H.; Lave, J.

    2016-12-01

    Pebble rounding caused by attrition is, beside chemical dissolution, breakage, and grain size segregation, one of the key processes controlling bedload downstream fining in rivers. Downstream changes in pebble geometry is subject of consideration since Aristotle (Krynine, 1960) and its measurement represent a challenge since the end of 19th century, leading to a long standing debate (Blott and Pye, 2008). A toolbox developed by Roussillon et al. (2009) operate on automatic computation of several shape and roundness indexes from images of 2D projection plan of pebbles disposed on a one meter square red board. In order to promote the tool for future applications, we tested the effects of pebble position on board, of picture resolution and treatment on three shape and roundness indexes. We also compared the downstream patterns of these indexes on two pebble samples of the same lithology collected on the Progo River (Indonesia) based on field observations (i) and experimentation (ii). Shape and roundness were measured on (i) 8 sites distributed over a distance of 36 km along the river, and (ii) ten times on a set of particules collected on the Progo spring and transported in an annular flume over the same distance. This travel distance was monitored using passive low frequency RFID system. Results show that pebble position does not have a significant effect on shape and roundness indexes but these indexes are sensible to picture resolutions and treatments so that a clear protocol must be considered for avoiding any observer bias. Downstream changes in roundness indexes are very similar in field and experimental conditions, while abrasion environments are distinct. Discontinuities observed in downstream river pattern but not in experimental one underlined changes in Progo River pebble roundness are probably caused by sediment supplied from tributaries or bank erosion. These results highlight the toolbox potential for diagnosing river systems function.

  18. National Water-Quality Assessment (NAWQA) area-characterization toolbox

    USGS Publications Warehouse

    Price, Curtis V.; Nakagaki, Naomi; Hitt, Kerie J.

    2010-01-01

    This is release 1.0 of the National Water-Quality Assessment (NAWQA) Area-Characterization Toolbox. These tools are designed to be accessed using ArcGIS Desktop software (versions 9.3 and 9.3.1). The toolbox is composed of a collection of custom tools that implement geographic information system (GIS) techniques used by the NAWQA Program to characterize aquifer areas, drainage basins, and sampled wells. These tools are built on top of standard functionality included in ArcGIS Desktop running at the ArcInfo license level. Most of the tools require a license for the ArcGIS Spatial Analyst extension. ArcGIS is a commercial GIS software system produced by ESRI, Inc. (http://www.esri.com). The NAWQA Area-Characterization Toolbox is not supported by ESRI, Inc. or its technical support staff. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

  19. Multimodal Imaging of Brain Connectivity Using the MIBCA Toolbox: Preliminary Application to Alzheimer's Disease

    NASA Astrophysics Data System (ADS)

    Ribeiro, André Santos; Lacerda, Luís Miguel; Silva, Nuno André da; Ferreira, Hugo Alexandre

    2015-06-01

    The Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox is a fully automated all-in-one connectivity analysis toolbox that offers both pre-processing, connectivity, and graph theory analysis of multimodal images such as anatomical, diffusion, and functional MRI, and PET. In this work, the MIBCA functionalities were used to study Alzheimer's Disease (AD) in a multimodal MR/PET approach. Materials and Methods: Data from 12 healthy controls, and 36 patients with EMCI, LMCI and AD (12 patients for each group) were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), including T1-weighted (T1-w), Diffusion Tensor Imaging (DTI) data, and 18F-AV-45 (florbetapir) dynamic PET data from 40-60 min post injection (4x5 min). Both MR and PET data were automatically pre-processed for all subjects using MIBCA. T1-w data was parcellated into cortical and subcortical regions-of-interest (ROIs), and the corresponding thicknesses and volumes were calculated. DTI data was used to compute structural connectivity matrices based on fibers connecting pairs of ROIs. Lastly, dynamic PET images were summed, and the relative Standard Uptake Values calculated for each ROI. Results: An overall higher uptake of 18F-AV-45, consistent with an increased deposition of beta-amyloid, was observed for the AD group. Additionally, patients showed significant cortical atrophy (thickness and volume) especially in the entorhinal cortex and temporal areas, and a significant increase in Mean Diffusivity (MD) in the hippocampus, amygdala and temporal areas. Furthermore, patients showed a reduction of fiber connectivity with the progression of the disease, especially for intra-hemispherical connections. Conclusion: This work shows the potential of the MIBCA toolbox for the study of AD, as findings were shown to be in agreement with the literature. Here, only structural changes and beta-amyloid accumulation were considered. Yet, MIBCA is further able to

  20. A fractured rock geophysical toolbox method selection tool

    USGS Publications Warehouse

    Day-Lewis, F. D.; Johnson, C.D.; Slater, L.D.; Robinson, J.L.; Williams, J.H.; Boyden, C.L.; Werkema, D.D.; Lane, J.W.

    2016-01-01

    Geophysical technologies have the potential to improve site characterization and monitoring in fractured rock, but the appropriate and effective application of geophysics at a particular site strongly depends on project goals (e.g., identifying discrete fractures) and site characteristics (e.g., lithology). No method works at every site or for every goal. New approaches are needed to identify a set of geophysical methods appropriate to specific project goals and site conditions while considering budget constraints. To this end, we present the Excel-based Fractured-Rock Geophysical Toolbox Method Selection Tool (FRGT-MST). We envision the FRGT-MST (1) equipping remediation professionals with a tool to understand what is likely to be realistic and cost-effective when contracting geophysical services, and (2) reducing applications of geophysics with unrealistic objectives or where methods are likely to fail.

  1. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances.

    PubMed Central

    Cronin, Mark T D; Walker, John D; Jaworska, Joanna S; Comber, Michael H I; Watts, Christopher D; Worth, Andrew P

    2003-01-01

    This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing. PMID:12896861

  2. CFS MATLAB toolbox: An experiment builder for continuous flash suppression (CFS) task.

    PubMed

    Nuutinen, Mikko; Mustonen, Terhi; Häkkinen, Jukka

    2017-09-15

    CFS toolbox is an open-source collection of MATLAB functions that utilizes PsychToolbox-3 (PTB-3). It is designed to allow a researcher to create and run continuous flash suppression experiments using a variety of experimental parameters (i.e., stimulus types and locations, noise characteristics, and experiment window settings). In a CFS experiment, one of the eyes at a time is presented with a dynamically changing noise pattern, while the other eye is concurrently presented with a static target stimulus, such as a Gabor patch. Due to the strong interocular suppression created by the dominant noise pattern mask, the target stimulus is rendered invisible for an extended duration. Very little knowledge of MATLAB is required for using the toolbox; experiments are generated by modifying csv files with the required parameters, and result data are output to text files for further analysis. The open-source code is available on the project page under a Creative Commons License ( http://www.mikkonuutinen.arkku.net/CFS_toolbox/ and https://bitbucket.org/mikkonuutinen/cfs_toolbox ).

  3. FracPaQ: A MATLAB™ toolbox for the quantification of fracture patterns

    NASA Astrophysics Data System (ADS)

    Healy, David; Rizzo, Roberto E.; Cornwell, David G.; Farrell, Natalie J. C.; Watkins, Hannah; Timms, Nick E.; Gomez-Rivas, Enrique; Smith, Michael

    2017-02-01

    The patterns of fractures in deformed rocks are rarely uniform or random. Fracture orientations, sizes, and spatial distributions often exhibit some kind of order. In detail, relationships may exist among the different fracture attributes, e.g. small fractures dominated by one orientation, larger fractures by another. These relationships are important because the mechanical (e.g. strength, anisotropy) and transport (e.g. fluids, heat) properties of rock depend on these fracture attributes and patterns. This paper describes FracPaQ, a new open source, cross-platform toolbox to quantify fracture patterns, including distributions in fracture attributes and their spatial variation. Software has been developed to quantify fracture patterns from 2-D digital images, such as thin section micrographs, geological maps, outcrop or aerial photographs or satellite images. The toolbox comprises a suite of MATLAB™ scripts based on previously published quantitative methods for the analysis of fracture attributes: orientations, lengths, intensity, density and connectivity. An estimate of permeability in 2-D is made using a parallel plate model. The software provides an objective and consistent methodology for quantifying fracture patterns and their variations in 2-D across a wide range of length scales, rock types and tectonic settings. The implemented methods presented are inherently scale independent, and a key task where applicable is analysing and integrating quantitative fracture pattern data from micro-to macro-scales. The toolbox was developed in MATLAB™ and the source code is publicly available on GitHub™ and the Mathworks™ FileExchange. The code runs on any computer with MATLAB installed, including PCs with Microsoft Windows, Apple Macs with Mac OS X, and machines running different flavours of Linux. The application, source code and sample input files are available in open repositories in the hope that other developers and researchers will optimise and extend the

  4. QSAR development and bioavailability determination: the toxicity of chloroanilines to the soil dwelling springtail Folsomia candida.

    PubMed

    Giesen, Daniel; van Gestel, Cornelis A M

    2013-03-01

    Quantitative structure-activity relationships (QSARs) are an established tool in environmental risk assessment and a valuable alternative to the exhaustive use of test animals under REACH. In this study a QSAR was developed for the toxicity of a series of six chloroanilines to the soil-dwelling collembolan Folsomia candida in standardized natural LUFA2.2 soil. Toxicity endpoints incorporated in the QSAR were the concentrations causing 10% (EC10) and 50% (EC50) reduction in reproduction of F. candida. Toxicity was based on concentrations in interstitial water estimated from nominal concentrations in the soil and published soil-water partition coefficients. Estimated effect concentrations were negatively correlated with the lipophilicity of the compounds. Interstitial water concentrations for both the EC10 and EC50 for four compounds were determined by using solid-phase microextraction (SPME). Measured and estimated concentrations were comparable only for tetra- and pentachloroaniline. With decreasing chlorination the disparity between modelled and actual concentrations increased. Optimisation of the QSAR therefore could not be accomplished, showing the necessity to move from total soil to (bio)available concentration measurements. Copyright © 2012 Elsevier Ltd. All rights reserved.

  5. Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

    PubMed

    Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

    2016-04-01

    The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  6. Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March 2009 version.

    PubMed

    Furuhama, A; Toida, T; Nishikawa, N; Aoki, Y; Yoshioka, Y; Shiraishi, H

    2010-07-01

    The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure-activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r(2 )> 0.7, RMSE 5, give acceptable q(2) values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.

  7. Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

    PubMed

    Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2012-08-01

    Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

  8. BOLDSync: a MATLAB-based toolbox for synchronized stimulus presentation in functional MRI.

    PubMed

    Joshi, Jitesh; Saharan, Sumiti; Mandal, Pravat K

    2014-02-15

    Precise and synchronized presentation of paradigm stimuli in functional magnetic resonance imaging (fMRI) is central to obtaining accurate information about brain regions involved in a specific task. In this manuscript, we present a new MATLAB-based toolbox, BOLDSync, for synchronized stimulus presentation in fMRI. BOLDSync provides a user friendly platform for design and presentation of visual, audio, as well as multimodal audio-visual (AV) stimuli in functional imaging experiments. We present simulation experiments that demonstrate the millisecond synchronization accuracy of BOLDSync, and also illustrate the functionalities of BOLDSync through application to an AV fMRI study. BOLDSync gains an advantage over other available proprietary and open-source toolboxes by offering a user friendly and accessible interface that affords both precision in stimulus presentation and versatility across various types of stimulus designs and system setups. BOLDSync is a reliable, efficient, and versatile solution for synchronized stimulus presentation in fMRI study. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. MNPBEM - A Matlab toolbox for the simulation of plasmonic nanoparticles

    NASA Astrophysics Data System (ADS)

    Hohenester, Ulrich; Trügler, Andreas

    2012-02-01

    MNPBEM is a Matlab toolbox for the simulation of metallic nanoparticles (MNP), using a boundary element method (BEM) approach. The main purpose of the toolbox is to solve Maxwell's equations for a dielectric environment where bodies with homogeneous and isotropic dielectric functions are separated by abrupt interfaces. Although the approach is in principle suited for arbitrary body sizes and photon energies, it is tested (and probably works best) for metallic nanoparticles with sizes ranging from a few to a few hundreds of nanometers, and for frequencies in the optical and near-infrared regime. The toolbox has been implemented with Matlab classes. These classes can be easily combined, which has the advantage that one can adapt the simulation programs flexibly for various applications. Program summaryProgram title: MNPBEM Catalogue identifier: AEKJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKJ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License v2 No. of lines in distributed program, including test data, etc.: 15 700 No. of bytes in distributed program, including test data, etc.: 891 417 Distribution format: tar.gz Programming language: Matlab 7.11.0 (R2010b) Computer: Any which supports Matlab 7.11.0 (R2010b) Operating system: Any which supports Matlab 7.11.0 (R2010b) RAM: ⩾1 GByte Classification: 18 Nature of problem: Solve Maxwell's equations for dielectric particles with homogeneous dielectric functions separated by abrupt interfaces. Solution method: Boundary element method using electromagnetic potentials. Running time: Depending on surface discretization between seconds and hours.

  10. CBP Toolbox Version 3.0 “Beta Testing” Performance Evaluation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Smith, III, F. G.

    2016-07-29

    One function of the Cementitious Barriers Partnership (CBP) is to assess available models of cement degradation and to assemble suitable models into a “Toolbox” that would be made available to members of the partnership, as well as the DOE Complex. To this end, SRNL and Vanderbilt University collaborated to develop an interface using the GoldSim software to the STADIUM @ code developed by SIMCO Technologies, Inc. and LeachXS/ORCHESTRA developed by Energy research Centre of the Netherlands (ECN). Release of Version 3.0 of the CBP Toolbox is planned in the near future. As a part of this release, an increased levelmore » of quality assurance for the partner codes and the GoldSim interface has been developed. This report documents results from evaluation testing of the ability of CBP Toolbox 3.0 to perform simulations of concrete degradation applicable to performance assessment of waste disposal facilities. Simulations of the behavior of Savannah River Saltstone Vault 2 and Vault 1/4 concrete subject to sulfate attack and carbonation over a 500- to 1000-year time period were run using a new and upgraded version of the STADIUM @ code and the version of LeachXS/ORCHESTRA released in Version 2.0 of the CBP Toolbox. Running both codes allowed comparison of results from two models which take very different approaches to simulating cement degradation. In addition, simulations of chloride attack on the two concretes were made using the STADIUM @ code. The evaluation sought to demonstrate that: 1) the codes are capable of running extended realistic simulations in a reasonable amount of time; 2) the codes produce “reasonable” results; the code developers have provided validation test results as part of their code QA documentation; and 3) the two codes produce results that are consistent with one another. Results of the evaluation testing showed that the three criteria listed above were met by the CBP partner codes. Therefore, it is concluded that the codes can be used

  11. QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals.

    PubMed

    Gramatica, Paola; Papa, Ester; Sangion, Alessandro

    2018-01-24

    The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.

  12. Ligand-based 3D QSAR analysis of reactivation potency of mono- and bis-pyridinium aldoximes toward VX-inhibited rat acetylcholinesterase.

    PubMed

    Dolezal, Rafael; Korabecny, Jan; Malinak, David; Honegr, Jan; Musilek, Kamil; Kuca, Kamil

    2015-03-01

    To predict unknown reactivation potencies of 12 mono- and bis-pyridinium aldoximes for VX-inhibited rat acetylcholinesterase (rAChE), three-dimensional quantitative structure-activity relationship (3D QSAR) analysis has been carried out. Utilizing molecular interaction fields (MIFs) calculated by molecular mechanical (MMFF94) and quantum chemical (B3LYP/6-31G*) methods, two satisfactory ligand-based CoMFA models have been developed: 1. R(2)=0.9989, Q(LOO)(2)=0.9090, Q(LTO)(2)=0.8921, Q(LMO(20%))(2)=0.8853, R(ext)(2)=0.9259, SDEP(ext)=6.8938; 2. R(2)=0.9962, Q(LOO)(2)=0.9368, Q(LTO)(2)=0.9298, Q(LMO(20%))(2)=0.9248, R(ext)(2)=0.8905, SDEP(ext)=6.6756. High statistical significance of the 3D QSAR models has been achieved through the application of several data noise reduction techniques (i.e. smart region definition SRD, fractional factor design FFD, uninformative/iterative variable elimination UVE/IVE) on the original MIFs. Besides the ligand-based CoMFA models, an alignment molecular set constructed by flexible molecular docking has been also studied. The contour maps as well as the predicted reactivation potencies resulting from 3D QSAR analyses help better understand which structural features are associated with increased reactivation potency of studied compounds. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Rad Toolbox User's Guide

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Eckerman, Keith F.; Sjoreen, Andrea L.

    2013-05-01

    The Radiological Toolbox software developed by Oak Ridge National Laboratory (ORNL) for U. S. Nuclear Regulatory Commission (NRC) is designed to provide electronic access to the vast and varied data that underlies the field of radiation protection. These data represent physical, chemical, anatomical, physiological, and mathematical parameters detailed in various handbooks which a health physicist might consult while in his office. The initial motivation for the software was to serve the needs of the health physicist away from his office and without access to his handbooks; e.g., NRC inspectors. The earlier releases of the software were widely used and acceptedmore » around the world by not only practicing health physicist but also those within educational programs. This release updates the software to accommodate changes in Windows operating systems and, in some aspects, radiation protection. This release has been tested on Windows 7 and 8 and on 32- and 64-bit machines. The nuclear decay data has been updated and thermal neutron capture cross sections and cancer risk coefficients have been included. This document and the software’s user’s guide provide further details and documentation of the information captured within the Radiological Toolbox.« less

  14. First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

    NASA Astrophysics Data System (ADS)

    Pandit, Amit; Sengupta, Sagnik; Krishnan, Mena Asha; Reddy, Ramesh B.; Sharma, Rajesh; Venkatesh, Chelvam

    2018-05-01

    suggest that Arg210, Asn257, Gly518, Tyr552, Lys699, and Tyr700 amino acid residues may play a crucial role in GCPII inhibition. Molecular Dynamics Simulation provides information about docked pose stability of DCIBzL. By combination of CoMFA-CoMSIA field analysis and docking interaction analysis studies, conclusive SAR was generated for urea based derivatives based on which GCPII inhibitor 7 was designed and chemically synthesized in our laboratory. Evaluation of GCPII inhibitory activity of 7 by performing NAALADase assay provided IC50 value of 113 nM which is in close agreement with in silico predicted value (119 nM). Thus we have successfully validated our 3D-QSAR and molecular docking based designing of GCPII inhibitors methodology through biological experiments. This conclusive SAR would be helpful to generate novel and more potent GCPII inhibitors for drug delivery applications.

  15. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.

    PubMed

    Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander; Zhu, Hao

    2017-06-30

    Numerous chemical data sets have become available for quantitative structure-activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

  16. Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

    PubMed Central

    2017-01-01

    Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting. PMID:28691113

  17. Beware of external validation! - A Comparative Study of Several Validation Techniques used in QSAR Modelling.

    PubMed

    Majumdar, Subhabrata; Basak, Subhash C

    2018-04-26

    Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but large number of predictors remains suspect. Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has high value of p but small n (i.e. n < p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. We compared four validation methods: leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Drinking Water Cyanotoxin Risk Communication Toolbox

    EPA Pesticide Factsheets

    The drinking water cyanotoxin risk communication toolbox is a ready-to-use, “one-stop-shop” to support public water systems, states, and local governments in developing, as they deem appropriate, their own risk communication materials.

  19. Air Sensor Toolbox for Citizen Scientists

    EPA Pesticide Factsheets

    EPA’s Air Sensor Toolbox provides information and guidance on new low-cost compact technologies for measuring air quality. It provides information to help citizens more effectively and accurately collect air quality data in their community.

  20. Air Sensor Toolbox: Resources and Funding

    EPA Pesticide Factsheets

    EPA’s Air Sensor Toolbox provides information and guidance on new low-cost compact technologies for measuring air quality. It provides information to help citizens more effectively and accurately collect air quality data in their community.

  1. QSAR Methods.

    PubMed

    Gini, Giuseppina

    2016-01-01

    In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

  2. Modern CACSD using the Robust-Control Toolbox

    NASA Technical Reports Server (NTRS)

    Chiang, Richard Y.; Safonov, Michael G.

    1989-01-01

    The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

  3. QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

    PubMed

    Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

    2013-06-01

    Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Esposito, Emilio Xavier, E-mail: emilio@exeResearch.com; The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045; Hopfinger, Anton J., E-mail: hopfingr@gmail.com

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted frommore » the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models.« less

  5. An ethics toolbox for neurotechnology.

    PubMed

    Farah, Martha J

    2015-04-08

    Advances in neurotechnology will raise new ethical dilemmas, to which scientists and the rest of society must respond. Here I present a "toolbox" of concepts to help us analyze these issues and communicate with each other about them across differences of ethical intuition. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. MMM: A toolbox for integrative structure modeling.

    PubMed

    Jeschke, Gunnar

    2018-01-01

    Structural characterization of proteins and their complexes may require integration of restraints from various experimental techniques. MMM (Multiscale Modeling of Macromolecules) is a Matlab-based open-source modeling toolbox for this purpose with a particular emphasis on distance distribution restraints obtained from electron paramagnetic resonance experiments on spin-labelled proteins and nucleic acids and their combination with atomistic structures of domains or whole protomers, small-angle scattering data, secondary structure information, homology information, and elastic network models. MMM does not only integrate various types of restraints, but also various existing modeling tools by providing a common graphical user interface to them. The types of restraints that can support such modeling and the available model types are illustrated by recent application examples. © 2017 The Protein Society.

  7. New public QSAR model for carcinogenicity

    PubMed Central

    2010-01-01

    Background One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. Results Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. Conclusion Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions

  8. SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    EPA Science Inventory

    Abstract

    SAR/QSAR MODELS FOR TOXICITY PREDICTION: APPROACHES AND NEW DIRECTIONS

    Risk assessment typically incorporates some relevant toxicity information upon which to base a sound estimation for a chemical of concern. However, there are many circumstances in whic...

  9. AQUATIC TOXICITY MODE OF ACTION STUDIES APPLIED TO QSAR DEVELOPMENT

    EPA Science Inventory

    A series of QSAR models for predicting fish acute lethality were developed using systematically collected data on more than 600 chemicals. These models were developed based on the assumption that chemicals producing toxicity through a common mechanism will have commonality in the...

  10. Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates.

    PubMed

    Dovlo, Edem; Baddour, Natalie

    2015-01-01

    The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: •The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms.•The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform.•The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time.

  11. An examination of data quality on QSAR Modeling in regards ...

    EPA Pesticide Factsheets

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to develop and validate QSAR models. We have focused our efforts on the widely used EPISuite software that was initially developed over two decades ago and, specifically, on the PHYSPROP dataset used to train the EPISuite prediction models. This presentation will review our approaches to examining key datasets, the delivery of curated data and the development of machine-learning models for thirteen separate property endpoints of interest to environmental science. We will also review how these data will be made freely accessible to the community via a new “chemistry dashboard”. This abstract does not reflect U.S. EPA policy. presentation at UNC-CH.

  12. The CatchMod toolbox: easy and guided access to ICT tools for Water Framework Directive implementation.

    PubMed

    van Griensven, A; Vanrolleghem, P A

    2006-01-01

    Web-based toolboxes are handy tools to inform experienced users of existing software in their disciplines. However, for the implementation of the Water Framework Directive, a much more diverse public (water managers, consultancy firms, scientists, etc.) will ask for a very wide diversity of Information and Communication Technology (ICT) tools. It is obvious that the users of a web-based ICT-toolbox providing all this will not be experts in all of the disciplines and that a toolbox for ICT tools for Water Framework Directive implementation should thus go beyond just making interesting web-links. To deal with this issue, expert knowledge is brought to the users through the incorporation of visitor-geared guidance (materials) in the Harmoni-CA toolbox. Small workshops of expert teams were organized to deliver documents explaining why the tools are important, when they are required and what activity they support/perform, as well as a categorization of the multitude of available tools. An integration of this information in the web-based toolbox helps the users to browse through a toolbox containing tools, reports, guidance documents and interesting links. The Harmoni-CA toolbox thus provides not only a virtual toolbox, but incorporates a virtual expert as well.

  13. QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.

    PubMed

    Qin, Li-Tang; Chen, Yu-Han; Zhang, Xin; Mo, Ling-Yun; Zeng, Hong-Hu; Liang, Yan-Peng

    2018-05-01

    Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC 50 ) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Alves, Vinicius M.; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599; Muratov, Eugene

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, wemore » found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between

  15. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.

    PubMed

    Toropov, Andrey A; Toropova, Alla P

    2015-04-01

    Available on the Internet, the CORAL software (http://www.insilico.eu/coral) has been used to build up quasi-quantitative structure-activity relationships (quasi-QSAR) for prediction of mutagenic potential of multi-walled carbon-nanotubes (MWCNTs). In contrast with the previous models built up by CORAL which were based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) the quasi-QSARs based on the representation of conditions (not on the molecular structure) such as concentration, presence (absence) S9 mix, the using (or without the using) of preincubation were encoded by so-called quasi-SMILES. The statistical characteristics of these models (quasi-QSARs) for three random splits into the visible training set and test set and invisible validation set are the following: (i) split 1: n=13, r(2)=0.8037, q(2)=0.7260, s=0.033, F=45 (training set); n=5, r(2)=0.9102, s=0.071 (test set); n=6, r(2)=0.7627, s=0.044 (validation set); (ii) split 2: n=13, r(2)=0.6446, q(2)=0.4733, s=0.045, F=20 (training set); n=5, r(2)=0.6785, s=0.054 (test set); n=6, r(2)=0.9593, s=0.032 (validation set); and (iii) n=14, r(2)=0.8087, q(2)=0.6975, s=0.026, F=51 (training set); n=5, r(2)=0.9453, s=0.074 (test set); n=5, r(2)=0.8951, s=0.052 (validation set). Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

    PubMed

    Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

    2016-01-01

    Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

  17. QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

    PubMed Central

    Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

    2009-01-01

    A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

  18. Application of 3D-QSAR, Pharmacophore, and Molecular Docking in the Molecular Design of Diarylpyrimidine Derivatives as HIV-1 Nonnucleoside Reverse Transcriptase Inhibitors.

    PubMed

    Liu, Genyan; Wang, Wenjie; Wan, Youlan; Ju, Xiulian; Gu, Shuangxi

    2018-05-11

    Diarylpyrimidines (DAPYs), acting as HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs), have been considered to be one of the most potent drug families in the fight against acquired immunodeficiency syndrome (AIDS). To better understand the structural requirements of HIV-1 NNRTIs, three-dimensional quantitative structure⁻activity relationship (3D-QSAR), pharmacophore, and molecular docking studies were performed on 52 DAPY analogues that were synthesized in our previous studies. The internal and external validation parameters indicated that the generated 3D-QSAR models, including comparative molecular field analysis (CoMFA, q 2 = 0.679, R 2 = 0.983, and r pred 2 = 0.884) and comparative molecular similarity indices analysis (CoMSIA, q 2 = 0.734, R 2 = 0.985, and r pred 2 = 0.891), exhibited good predictive abilities and significant statistical reliability. The docking results demonstrated that the phenyl ring at the C₄-position of the pyrimidine ring was better than the cycloalkanes for the activity, as the phenyl group was able to participate in π⁻π stacking interactions with the aromatic residues of the binding site, whereas the cycloalkanes were not. The pharmacophore model and 3D-QSAR contour maps provided significant insights into the key structural features of DAPYs that were responsible for the activity. On the basis of the obtained information, a series of novel DAPY analogues of HIV-1 NNRTIs with potentially higher predicted activity was designed. This work might provide useful information for guiding the rational design of potential HIV-1 NNRTI DAPYs.

  19. Basic Radar Altimetry Toolbox: tools to teach altimetry for ocean

    NASA Astrophysics Data System (ADS)

    Rosmorduc, Vinca; Benveniste, Jerome; Bronner, Emilie; Niemeijer, Sander; Lucas, Bruno Manuel; Dinardo, Salvatore

    2013-04-01

    The Basic Radar Altimetry Toolbox is an "all-altimeter" collection of tools, tutorials and documents designed to facilitate the use of radar altimetry data, including the next mission to be launched, CryoSat. It has been available from April 2007, and had been demonstrated during training courses and scientific meetings. More than 2000 people downloaded it (January 2013), with many "newcomers" to altimetry among them. Users' feedbacks, developments in altimetry, and practice, showed that new interesting features could be added. Some have been added and/or improved in version 2 and 3. Others are in discussion for the future, including addition of the future Sentinel-3. The Basic Radar Altimetry Toolbox is able: - to read most distributed radar altimetry data, including the one from future missions like Saral, - to perform some processing, data editing and statistic, - and to visualize the results. It can be used at several levels/several ways, including as an educational tool, with the graphical user interface As part of the Toolbox, a Radar Altimetry Tutorial gives general information about altimetry, the technique involved and its applications, as well as an overview of past, present and future missions, including information on how to access data and additional software and documentation. It also presents a series of data use cases, covering all uses of altimetry over ocean, cryosphere and land, showing the basic methods for some of the most frequent manners of using altimetry data. Example from education uses will be presented, and feedback from those who used it as such will be most welcome. BRAT is developed under contract with ESA and CNES. It is available at http://www.altimetry.info and http://earth.esa.int/brat/

  20. TRIQS: A toolbox for research on interacting quantum systems

    NASA Astrophysics Data System (ADS)

    Parcollet, Olivier; Ferrero, Michel; Ayral, Thomas; Hafermann, Hartmut; Krivenko, Igor; Messio, Laura; Seth, Priyanka

    2015-11-01

    We present the TRIQS library, a Toolbox for Research on Interacting Quantum Systems. It is an open-source, computational physics library providing a framework for the quick development of applications in the field of many-body quantum physics, and in particular, strongly-correlated electronic systems. It supplies components to develop codes in a modern, concise and efficient way: e.g. Green's function containers, a generic Monte Carlo class, and simple interfaces to HDF5. TRIQS is a C++/Python library that can be used from either language. It is distributed under the GNU General Public License (GPLv3). State-of-the-art applications based on the library, such as modern quantum many-body solvers and interfaces between density-functional-theory codes and dynamical mean-field theory (DMFT) codes are distributed along with it.

  1. New p-methylsulfonamido phenylethylamine analogues as class III antiarrhythmic agents: design, synthesis, biological assay, and 3D-QSAR analysis.

    PubMed

    Liu, Hong; Ji, Ming; Luo, Xiaomin; Shen, Jianhua; Huang, Xiaoqin; Hua, Weiyi; Jiang, Hualiang; Chen, Kaixian

    2002-07-04

    Class III antiarrhythmic agents selectively delay the effective refractory period (ERP) and increase the transmembrane action potential duration (APD). Using dofetilide (2) as a template of class III antiarrhythmic agents, we designed and synthesized 16 methylsulfonamido phenylethylamine analogues (4a-d and 5a-l). Pharmacological assay indicated that all of these compounds showed activity for increasing the ERP in isolated animal atrium; among them, the effective concentration of compound 4a is 1.6 x 10(-8) mol/L in increasing ERP by 10 ms, slightly less potent than that of 2, 1.1 x 10(-8) mol/L. Compound 4a also produced a slightly lower change in ERP at 10(-5) M, DeltaERP% = 17.5% (DeltaERP% = 24.0% for dofetilide). On the basis of this bioassay result, these 16 compounds together with dofetilide were investigated by the three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques of comparative molecular field analysis (CoMFA), comparative molecular similarity index analysis (CoMSIA), and the hologram QSAR (HQSAR). The 3D-QSAR models were tested with another 11 compounds (4e-h and 5m-s) that we synthesized later. Results revealed that the CoMFA, CoMSIA, and HQSAR predicted activities for the 11 newly synthesized compounds that have a good correlation with their experimental value, r(2) = 0.943, 0.891, and 0.809 for the three QSAR models, respectively. This indicates that the 3D-QSAR models proved a good predictive ability and could describe the steric, electrostatic, and hydrophobic requirements for recognition forces of the receptor site. On the basis of these results, we designed and synthesized another eight new analogues of methanesulfonamido phenylethyamine (6a-h) according to the clues provided by the 3D-QSAR analyses. Pharmacological assay indicated that the effective concentrations of delaying the ERP by 10 ms of these newly designed compounds correlated well with the 3D-QSAR predicted values. It is remarkable that the percent

  2. Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

    PubMed

    Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

    2011-10-01

    The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors.

    PubMed

    Tarasova, Olga A; Urusova, Aleksandra F; Filimonov, Dmitry A; Nicklaus, Marc C; Zakharov, Alexey V; Poroikov, Vladimir V

    2015-07-27

    Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

  4. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  5. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

    PubMed Central

    Schellenberger, Jan; Que, Richard; Fleming, Ronan M. T.; Thiele, Ines; Orth, Jeffrey D.; Feist, Adam M.; Zielinski, Daniel C.; Bordbar, Aarash; Lewis, Nathan E.; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R.; Palsson, Bernhard Ø.

    2012-01-01

    Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods. PMID:21886097

  6. The MONGOOSE Rational Arithmetic Toolbox.

    PubMed

    Le, Christopher; Chindelevitch, Leonid

    2018-01-01

    The modeling of metabolic networks has seen a rapid expansion following the complete sequencing of thousands of genomes. The constraint-based modeling framework has emerged as one of the most popular approaches to reconstructing and analyzing genome-scale metabolic models. Its main assumption is that of a quasi-steady-state, requiring that the production of each internal metabolite be balanced by its consumption. However, due to the multiscale nature of the models, the large number of reactions and metabolites, and the use of floating-point arithmetic for the stoichiometric coefficients, ensuring that this assumption holds can be challenging.The MONGOOSE toolbox addresses this problem by using rational arithmetic, thus ensuring that models are analyzed in a reproducible manner and consistently with modeling assumptions. In this chapter we present a protocol for the complete analysis of a metabolic network model using the MONGOOSE toolbox, via its newly developed GUI, and describe how it can be used as a model-checking platform both during and after the model construction process.

  7. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    PubMed

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. SSOAP Toolbox Enhancements and Case Study

    EPA Science Inventory

    Recognizing the need for tools to support the development of sanitary sewer overflow (SSO) control plans, in October 2009 the U.S. Environmental Protection Agency (EPA) released the first version of the Sanitary Sewer Overflow Analysis and Planning (SSOAP) Toolbox. This first ve...

  9. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

    PubMed

    Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

    2016-11-29

    In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

  10. A Data Analysis Toolbox for Modeling the Global Food-Energy-Water Nexus

    NASA Astrophysics Data System (ADS)

    AghaKouchak, A.; Sadegh, M.; Mallakpour, I.

    2017-12-01

    Water, Food and energy systems are highly interconnected. More than seventy percent of global water resource is used for food production. Water withdrawal, purification, and transfer systems are energy intensive. Furthermore, energy generation strongly depends on water availability. Therefore, considering the interactions in the nexus of water, food and energy is crucial for sustainable management of available resources. In this presentation, we introduce a user-friendly data analysis toolbox that mines the available global data on food, energy and water, and analyzes their interactions. This toolbox provides estimates of water footprint for a wide range of food types in different countries and also approximates the required energy and water resources. The toolbox also provides estimates of the corresponding emissions and biofuel production of different crops. In summary, this toolbox allows evaluating dependencies of the food, energy, and water systems at the country scale. We present global analysis of the interactions between water, food and energy from different perspectives including efficiency and diversity of resources use.

  11. The laboratory test utilization management toolbox

    PubMed Central

    Baird, Geoffrey

    2014-01-01

    Efficiently managing laboratory test utilization requires both ensuring adequate utilization of needed tests in some patients and discouraging superfluous tests in other patients. After the difficult clinical decision is made to define the patients that do and do not need a test, a wealth of interventions are available to the clinician and laboratorian to help guide appropriate utilization. These interventions are collectively referred to here as the utilization management toolbox. Experience has shown that some tools in the toolbox are weak and other are strong, and that tools are most effective when many are used simultaneously. While the outcomes of utilization management studies are not always as concrete as may be desired, what data is available in the literature indicate that strong utilization management interventions are safe and effective measures to improve patient health and reduce waste in an era of increasing financial pressure. PMID:24969916

  12. 3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.

    PubMed

    Pandey, Gyanendra; Saxena, Anil K

    2006-01-01

    A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

  13. Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors.

    PubMed

    Dai, Yujie; Wang, Qiang; Zhang, Xiuli; Jia, Shiru; Zheng, Heng; Feng, Dacheng; Yu, Peng

    2010-12-01

    In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

  14. An image analysis toolbox for high-throughput C. elegans assays

    PubMed Central

    Wählby, Carolina; Kamentsky, Lee; Liu, Zihan H.; Riklin-Raviv, Tammy; Conery, Annie L.; O’Rourke, Eyleen J.; Sokolnicki, Katherine L.; Visvikis, Orane; Ljosa, Vebjorn; Irazoqui, Javier E.; Golland, Polina; Ruvkun, Gary; Ausubel, Frederick M.; Carpenter, Anne E.

    2012-01-01

    We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease. PMID:22522656

  15. An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave.

    PubMed

    Silva, Ikaro; Moody, George B

    The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.

  16. SBEToolbox: A Matlab Toolbox for Biological Network Analysis

    PubMed Central

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J.

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases. PMID:24027418

  17. SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

    PubMed

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

  18. Synthetic Biology Toolbox for Controlling Gene Expression in the Cyanobacterium Synechococcus sp. strain PCC 7002

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Markley, Andrew L.; Begemann, Matthew B.; Clarke, Ryan E.

    The application of synthetic biology requires characterized tools to precisely control gene expression. This toolbox of genetic parts previously did not exist for the industrially promising cyanobacterium, Synechococcus sp. strain PCC 7002. To address this gap, two orthogonal constitutive promoter libraries, one based on a cyanobacterial promoter and the other ported from Escherichia coli, were built and tested in PCC 7002. The libraries demonstrated 3 and 2.5 log dynamic ranges, respectively, but correlated poorly with E. coli expression levels. These promoter libraries were then combined to create and optimize a series of IPTG inducible cassettes. The resultant induction system hadmore » a 48-fold dynamic range and was shown to out-perform P trc constructs. Finally, a RBS library was designed and tested in PCC 7002. The presented synthetic biology toolbox will enable accelerated engineering of PCC 7002.« less

  19. Synthetic Biology Toolbox for Controlling Gene Expression in the Cyanobacterium Synechococcus sp. strain PCC 7002

    DOE PAGES

    Markley, Andrew L.; Begemann, Matthew B.; Clarke, Ryan E.; ...

    2014-09-12

    The application of synthetic biology requires characterized tools to precisely control gene expression. This toolbox of genetic parts previously did not exist for the industrially promising cyanobacterium, Synechococcus sp. strain PCC 7002. To address this gap, two orthogonal constitutive promoter libraries, one based on a cyanobacterial promoter and the other ported from Escherichia coli, were built and tested in PCC 7002. The libraries demonstrated 3 and 2.5 log dynamic ranges, respectively, but correlated poorly with E. coli expression levels. These promoter libraries were then combined to create and optimize a series of IPTG inducible cassettes. The resultant induction system hadmore » a 48-fold dynamic range and was shown to out-perform P trc constructs. Finally, a RBS library was designed and tested in PCC 7002. The presented synthetic biology toolbox will enable accelerated engineering of PCC 7002.« less

  20. Building a symbolic computer algebra toolbox to compute 2D Fourier transforms in polar coordinates

    PubMed Central

    Dovlo, Edem; Baddour, Natalie

    2015-01-01

    The development of a symbolic computer algebra toolbox for the computation of two dimensional (2D) Fourier transforms in polar coordinates is presented. Multidimensional Fourier transforms are widely used in image processing, tomographic reconstructions and in fact any application that requires a multidimensional convolution. By examining a function in the frequency domain, additional information and insights may be obtained. The advantages of our method include: • The implementation of the 2D Fourier transform in polar coordinates within the toolbox via the combination of two significantly simpler transforms. • The modular approach along with the idea of lookup tables implemented help avoid the issue of indeterminate results which may occur when attempting to directly evaluate the transform. • The concept also helps prevent unnecessary computation of already known transforms thereby saving memory and processing time. PMID:26150988

  1. A CRISPR/Cas9 Toolbox for Multiplexed Plant Genome Editing and Transcriptional Regulation.

    PubMed

    Lowder, Levi G; Zhang, Dengwei; Baltes, Nicholas J; Paul, Joseph W; Tang, Xu; Zheng, Xuelian; Voytas, Daniel F; Hsieh, Tzung-Fu; Zhang, Yong; Qi, Yiping

    2015-10-01

    The relative ease, speed, and biological scope of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated Protein9 (Cas9)-based reagents for genomic manipulations are revolutionizing virtually all areas of molecular biosciences, including functional genomics, genetics, applied biomedical research, and agricultural biotechnology. In plant systems, however, a number of hurdles currently exist that limit this technology from reaching its full potential. For example, significant plant molecular biology expertise and effort is still required to generate functional expression constructs that allow simultaneous editing, and especially transcriptional regulation, of multiple different genomic loci or multiplexing, which is a significant advantage of CRISPR/Cas9 versus other genome-editing systems. To streamline and facilitate rapid and wide-scale use of CRISPR/Cas9-based technologies for plant research, we developed and implemented a comprehensive molecular toolbox for multifaceted CRISPR/Cas9 applications in plants. This toolbox provides researchers with a protocol and reagents to quickly and efficiently assemble functional CRISPR/Cas9 transfer DNA constructs for monocots and dicots using Golden Gate and Gateway cloning methods. It comes with a full suite of capabilities, including multiplexed gene editing and transcriptional activation or repression of plant endogenous genes. We report the functionality and effectiveness of this toolbox in model plants such as tobacco (Nicotiana benthamiana), Arabidopsis (Arabidopsis thaliana), and rice (Oryza sativa), demonstrating its utility for basic and applied plant research. © 2015 American Society of Plant Biologists. All Rights Reserved.

  2. Quantitative studies on structure-ORAC relationships of anthocyanins from eggplant and radish using 3D-QSAR.

    PubMed

    Jing, Pu; Zhao, Shujuan; Ruan, Siyu; Sui, Zhongquan; Chen, Lihong; Jiang, Linlei; Qian, Bingjun

    2014-02-15

    The 3-dimensional quantitative structure activity relationship (3D-QSAR) models were established from 21 anthocyanins based on their oxygen radical absorbing capacity (ORAC) and were applied to predict anthocyanins in eggplant and radish for their ORAC values. The cross-validated q(2)=0.857/0.729, non-cross-validated r(2) = 0.958/0.856, standard error of estimate = 0.153/0.134, and F = 73.267/19.247 were for the best QSAR (CoMFA/CoMSIA) models, where the correlation coefficient r(2)pred = 0.998/0.997 (>0.6) indicated a high predictive ability for each. Additionally, the contour map results suggested that structural characteristics of anthocyanins favourable for the high ORAC. Four anthocyanins from eggplant and radish have been screened based on the QSAR models. Pelargonidin-3-[(6''-p-coumaroyl)-glucosyl(2 → 1)glucoside]-5-(6''-malonyl)-glucoside, delphinidin-3-rutinoside-5-glucoside, and delphinidin-3-[(4''-p-coumaroyl)-rhamnosyl(1 → 6)glucoside]-5-glucoside potential with high ORAC based the QSAR models were isolated and also confirmed for their relative high antioxidant ability, which might attribute to the bulky and/or electron-donating substituent at the 3-position in the C ring or/and hydrogen bond donor group/electron donating group on the R1 position in the B ring. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Integrating hidden Markov model and PRAAT: a toolbox for robust automatic speech transcription

    NASA Astrophysics Data System (ADS)

    Kabir, A.; Barker, J.; Giurgiu, M.

    2010-09-01

    An automatic time-aligned phone transcription toolbox of English speech corpora has been developed. Especially the toolbox would be very useful to generate robust automatic transcription and able to produce phone level transcription using speaker independent models as well as speaker dependent models without manual intervention. The system is based on standard Hidden Markov Models (HMM) approach and it was successfully experimented over a large audiovisual speech corpus namely GRID corpus. One of the most powerful features of the toolbox is the increased flexibility in speech processing where the speech community would be able to import the automatic transcription generated by HMM Toolkit (HTK) into a popular transcription software, PRAAT, and vice-versa. The toolbox has been evaluated through statistical analysis on GRID data which shows that automatic transcription deviates by an average of 20 ms with respect to manual transcription.

  4. A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure.

    PubMed

    Shen, Yi; Dai, Wei; Richards, Virginia M

    2015-03-01

    A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given.

  5. A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure

    PubMed Central

    Richards, V. M.; Dai, W.

    2014-01-01

    A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given. PMID:24671826

  6. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

    NASA Astrophysics Data System (ADS)

    Andersson, C. David; Hillgren, J. Mikael; Lindgren, Cecilia; Qian, Weixing; Akfur, Christine; Berg, Lotta; Ekström, Fredrik; Linusson, Anna

    2015-03-01

    Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

  7. Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) User's Guide

    NASA Technical Reports Server (NTRS)

    Chapman, Jeffryes W.; Lavelle, Thomas M.; May, Ryan D.; Litt, Jonathan S.; Guo, Ten-Huei

    2014-01-01

    The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) software package is an open source, MATLABSimulink toolbox (plug in) that can be used by industry professionals and academics for the development of thermodynamic and controls simulations.

  8. Basic Radar Altimetry Toolbox: Tools and Tutorial to Use Cryosat Data

    NASA Astrophysics Data System (ADS)

    Benveniste, J.; Bronner, E.; Dinardo, S.; Lucas, B. M.; Rosmorduc, V.; Earith, D.; Niemeijer, S.

    2011-12-01

    Radar altimetry is very much a technique expanding its applications. Even If quite a lot of effort has been invested for oceanography users, the use of Altimetry data for cryosphere application, especially with the new ESA CryoSat-2 mission data is still somehow tedious for new Altimetry data products users. ESA and CNES therfore developed the Basic Radar Altimetry Toolbox a few years ago, and are improving and upgrading it to fit new missions and the growing number of altimetry uses. The Basic Radar Altimetry Toolbox is an "all-altimeter" collection of tools, tutorials and documents designed to facilitate the use of radar altimetry data. The software is able: - to read most distributed radar altimetry data, from ERS-1 & 2, Topex/Poseidon, Geosat Follow-on, Jason-1, Envisat, Jason- 2, CryoSat, the future Saral missions and is ready for adaptation to Sentinel-3 products - to perform some processing, data editing and statistic, - and to visualize the results. It can be used at several levels/several ways: - as a data reading tool, with APIs for C, Fortran, Matlab and IDL - as processing/extraction routines, through the on-line command mode - as an educational and a quick-look tool, with the graphical user interface As part of the Toolbox, a Radar Altimetry Tutorial gives general information about altimetry, the technique involved and its applications, as well as an overview of past, present and future missions, including information on how to access data and additional software and documentation. It also presents a series of data use cases, covering all uses of altimetry over ocean, cryosphere and land, showing the basic methods for some of the most frequent manners of using altimetry data. It is an opportunity to teach remote sensing with practical training. It has been available since April 2007, and had been demonstrated during training courses and scientific meetings. About 2000 people downloaded it (Summer 2011), with many "newcomers" to altimetry among them

  9. Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

    PubMed

    Esposito, Emilio Xavier; Hopfinger, Anton J; Shao, Chi-Yu; Su, Bo-Han; Chen, Sing-Zuo; Tseng, Yufeng Jane

    2015-10-01

    Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development.

    PubMed

    Tetko, Igor V; Maran, Uko; Tropsha, Alexander

    2017-03-01

    Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling.

    PubMed

    Mansouri, K; Grulke, C M; Richard, A M; Judson, R S; Williams, A J

    2016-11-01

    The increasing availability of large collections of chemical structures and associated experimental data provides an opportunity to build robust QSAR models for applications in different fields. One common concern is the quality of both the chemical structure information and associated experimental data. Here we describe the development of an automated KNIME workflow to curate and correct errors in the structure and identity of chemicals using the publicly available PHYSPROP physicochemical properties and environmental fate datasets. The workflow first assembles structure-identity pairs using up to four provided chemical identifiers, including chemical name, CASRNs, SMILES, and MolBlock. Problems detected included errors and mismatches in chemical structure formats, identifiers and various structure validation issues, including hypervalency and stereochemistry descriptions. Subsequently, a machine learning procedure was applied to evaluate the impact of this curation process. The performance of QSAR models built on only the highest-quality subset of the original dataset was compared with the larger curated and corrected dataset. The latter showed statistically improved predictive performance. The final workflow was used to curate the full list of PHYSPROP datasets, and is being made publicly available for further usage and integration by the scientific community.

  12. QSAR studies of benzofuran/benzothiophene biphenyl derivatives as inhibitors of PTPase-1B

    PubMed Central

    Kaushik, D.; Kumar, R.; Saxena, A. K.

    2010-01-01

    Objectives: Insulin resistance is associated with a defect in protein tyrosine phosphorylation in the insulin signal transduction cascade. The PTPase enzyme dephosphorylates the active form of the insulin receptor and thus attenuates its tyrosine kinase activity, therefore, the need for a potent PTPase inhibitor exists, with the intention of which the QSAR was performed. Materials and Methods: Quantitative structure-activity relationship (QSAR) has been established on a series of 106 compounds considering 27 variables, for novel biphenyl analogs, using the SYSTAT (Version 7.0) software, for their protein tyrosine phosphatase (PTPase-1B) inhibitor activity, in order to understand the essential structural requirement for binding with the receptor. Results: Among several regression models, one per series was selected on the basis of a high correlation coefficient (r, 0.86), least standard deviation (s, 0.234), and a high value of significance for the maximum number of subjects (n, 101). Conclusions: The influence of the different physicochemical parameters of the substituents in various positions has been discussed by generating the best QSAR model using multiple regression analysis, and the information thus obtained from the present study can be used to design and predict more potent molecules as PTPase-1B inhibitors, prior to their synthesis. PMID:21814427

  13. Visualizing flow fields using acoustic Doppler current profilers and the Velocity Mapping Toolbox

    USGS Publications Warehouse

    Jackson, P. Ryan

    2013-01-01

    The purpose of this fact sheet is to provide examples of how the U.S. Geological Survey is using acoustic Doppler current profilers for much more than routine discharge measurements. These instruments are capable of mapping complex three-dimensional flow fields within rivers, lakes, and estuaries. Using the Velocity Mapping Toolbox to process the ADCP data allows detailed visualization of the data, providing valuable information for a range of studies and applications.

  14. A Michigan toolbox for mitigating traffic congestion.

    DOT National Transportation Integrated Search

    2011-09-30

    "Researchers created A Michigan Toolbox for Mitigating Traffic Congestion to be a useful desk reference : for practitioners and an educational tool for elected officials acting through public policy boards to better : understand the development, plan...

  15. The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.

    PubMed

    Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko

    2008-06-01

    One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

  16. RESPONSE PROTOCOL TOOLBOX: OVERVIEW, STATUS UPDATE, AND RELATIONSHIP TO OTHER WATER SECURITY PRODUCTS

    EPA Science Inventory

    The Response Protocol Toolbox was released by USEPA to address the complex, multi-faceted challenges of a water utility's planning and response to the threat or act of intentional contamination of drinking water (1). The Toolbox contains guidance that may be adopted voluntarily,...

  17. RESPONSE PROTOCOL TOOLBOX OVERVIEW, STATUS UPDATE, AND RELATIONSHIP TO OTHER WATER SECURITY PRODUCTS

    EPA Science Inventory

    The Response Protocol Toolbox was released by USEPA to address the complex, multi-faceted challenges of a water utility's planning and response to the threat or act of intentional contamination of drinking water (1). The Toolbox contains guidance that may be adopted voluntarily,...

  18. QSAR models based on quantum topological molecular similarity.

    PubMed

    Popelier, P L A; Smith, P J

    2006-07-01

    A new method called quantum topological molecular similarity (QTMS) was fairly recently proposed [J. Chem. Inf. Comp. Sc., 41, 2001, 764] to construct a variety of medicinal, ecological and physical organic QSAR/QSPRs. QTMS method uses quantum chemical topology (QCT) to define electronic descriptors drawn from modern ab initio wave functions of geometry-optimised molecules. It was shown that the current abundance of computing power can be utilised to inject realistic descriptors into QSAR/QSPRs. In this article we study seven datasets of medicinal interest : the dissociation constants (pK(a)) for a set of substituted imidazolines , the pK(a) of imidazoles , the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes , the influenza inhibition constants for a set of benzimidazoles , the interaction constants for a set of amides and the enzyme liver alcohol dehydrogenase , the natriuretic activity of sulphonamide carbonic anhydrase inhibitors and the toxicity of a series of benzyl alcohols. A partial least square analysis in conjunction with a genetic algorithm delivered excellent models. They are also able to highlight the active site, of the ligand or the molecule whose structure determines the activity. The advantages and limitations of QTMS are discussed.

  19. Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Pelt, Daniël M.; Gürsoy, Dogˇa; Palenstijn, Willem Jan

    2016-04-28

    The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it ismore » shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy's standard reconstruction method.« less

  20. Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data

    PubMed Central

    Pelt, Daniël M.; Gürsoy, Doǧa; Palenstijn, Willem Jan; Sijbers, Jan; De Carlo, Francesco; Batenburg, Kees Joost

    2016-01-01

    The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy’s standard reconstruction method. PMID:27140167

  1. PSYCHOACOUSTICS: a comprehensive MATLAB toolbox for auditory testing

    PubMed Central

    Soranzo, Alessandro; Grassi, Massimo

    2014-01-01

    PSYCHOACOUSTICS is a new MATLAB toolbox which implements three classic adaptive procedures for auditory threshold estimation. The first includes those of the Staircase family (method of limits, simple up-down and transformed up-down); the second is the Parameter Estimation by Sequential Testing (PEST); and the third is the Maximum Likelihood Procedure (MLP). The toolbox comes with more than twenty built-in experiments each provided with the recommended (default) parameters. However, if desired, these parameters can be modified through an intuitive and user friendly graphical interface and stored for future use (no programming skills are required). Finally, PSYCHOACOUSTICS is very flexible as it comes with several signal generators and can be easily extended for any experiment. PMID:25101013

  2. PSYCHOACOUSTICS: a comprehensive MATLAB toolbox for auditory testing.

    PubMed

    Soranzo, Alessandro; Grassi, Massimo

    2014-01-01

    PSYCHOACOUSTICS is a new MATLAB toolbox which implements three classic adaptive procedures for auditory threshold estimation. The first includes those of the Staircase family (method of limits, simple up-down and transformed up-down); the second is the Parameter Estimation by Sequential Testing (PEST); and the third is the Maximum Likelihood Procedure (MLP). The toolbox comes with more than twenty built-in experiments each provided with the recommended (default) parameters. However, if desired, these parameters can be modified through an intuitive and user friendly graphical interface and stored for future use (no programming skills are required). Finally, PSYCHOACOUSTICS is very flexible as it comes with several signal generators and can be easily extended for any experiment.

  3. Real time wind farm emulation using SimWindFarm toolbox

    NASA Astrophysics Data System (ADS)

    Topor, Marcel

    2016-06-01

    This paper presents a wind farm emulation solution using an open source Matlab/Simulink toolbox and the National Instruments cRIO platform. This work is based on the Aeolus SimWindFarm (SWF) toolbox models developed at Aalborg university, Denmark. Using the Matlab Simulink models developed in SWF, the modeling code can be exported to a real time model using the NI Veristand model framework and the resulting code is integrated as a hardware in the loop control on the NI 9068 platform.

  4. RESPONSE PROTOCOL TOOLBOX: OVERVIEW, STATUS UPDATE, AND RELATIONSHIP TO OTHER WATER SECURITY PRODUCTS

    EPA Science Inventory

    The Response Protocol Toolbox was released by USEPA to address the complex, multi-faceted challenges of a water utility's planning and response to the threat or act of intentional contamination of drinking water(1). The Toolbox contains guidance that may be adopted voluntarily, a...

  5. Ironbound Community Citizen Science Toolbox Fact Sheet

    EPA Pesticide Factsheets

    EPA is partnering with Newark’s Ironbound Community Corporation (ICC) to design, develop, and pilot a Citizen Science Toolbox that will enable communities to collect their own environmental data and increase their ability to understand local conditions.

  6. 4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

    PubMed

    Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

    2012-02-01

    A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

  7. Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters.

    PubMed

    Rácz, A; Bajusz, D; Héberger, K

    2015-01-01

    Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.

  8. A Toolbox to Improve Algorithms for Insulin-Dosing Decision Support

    PubMed Central

    Donsa, K.; Plank, J.; Schaupp, L.; Mader, J. K.; Truskaller, T.; Tschapeller, B.; Höll, B.; Spat, S.; Pieber, T. R.

    2014-01-01

    Summary Background Standardized insulin order sets for subcutaneous basal-bolus insulin therapy are recommended by clinical guidelines for the inpatient management of diabetes. The algorithm based GlucoTab system electronically assists health care personnel by supporting clinical workflow and providing insulin-dose suggestions. Objective To develop a toolbox for improving clinical decision-support algorithms. Methods The toolbox has three main components. 1) Data preparation: Data from several heterogeneous sources is extracted, cleaned and stored in a uniform data format. 2) Simulation: The effects of algorithm modifications are estimated by simulating treatment workflows based on real data from clinical trials. 3) Analysis: Algorithm performance is measured, analyzed and simulated by using data from three clinical trials with a total of 166 patients. Results Use of the toolbox led to algorithm improvements as well as the detection of potential individualized subgroup-specific algorithms. Conclusion These results are a first step towards individualized algorithm modifications for specific patient subgroups. PMID:25024768

  9. 3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

    NASA Astrophysics Data System (ADS)

    Assefa, Haregewein; Kamath, Shantaram; Buolamwini, John K.

    2003-08-01

    The overexpression and/or mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase has been observed in many human solid tumors, and is under intense investigation as a novel anticancer molecular target. Comparative 3D-QSAR analyses using different alignments were undertaken employing comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) for 122 anilinoquinazoline and 50 anilinoquinoline inhibitors of EGFR kinase. The SYBYL multifit alignment rule was applied to three different conformational templates, two obtained from a MacroModel Monte Carlo conformational search, and one from the bound conformation of erlotinib in complex with EGFR in the X-ray crystal structure. In addition, a flexible ligand docking alignment obtained with the GOLD docking program, and a novel flexible receptor-guided consensus dynamics alignment obtained with the DISCOVER program in the INSIGHTII modeling package were also investigated. 3D-QSAR models with q2 values up to 0.70 and r2 values up to 0.97 were obtained. Among the 4-anilinoquinazoline set, the q2 values were similar, but the ability of the different conformational models to predict the activities of an external test set varied considerably. In this regard, the model derived using the X-ray crystallographically determined bioactive conformation of erlotinib afforded the best predictive model. Electrostatic, hydrophobic and H-bond donor descriptors contributed the most to the QSAR models of the 4-anilinoquinazolines, whereas electrostatic, hydrophobic and H-bond acceptor descriptors contributed the most to the 4-anilinoquinoline QSAR, particularly the H-bond acceptor descriptor. A novel receptor-guided consensus dynamics alignment has also been introduced for 3D-QSAR studies. This new alignment method may incorporate to some extent ligand-receptor induced fit effects into 3D-QSAR models.

  10. Latest advances in molecular topology applications for drug discovery.

    PubMed

    Zanni, Riccardo; Galvez-Llompart, Maria; García-Domenech, Ramón; Galvez, Jorge

    2015-01-01

    Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. In the last decade, its application has become more and more popular among the leading research groups in the field of quantitative structure-activity relationships (QSAR) and drug design. This has, in turn, contributed to the rapid development of new techniques and applications of MT in QSAR studies, as well as the introduction of new topological indices. This review collates the main innovative techniques in the field of MT and provides a description of the novel topological indices recently introduced, through an exhaustive recompilation of the most significant works carried out by the leading research groups in the field of drug design and discovery. The objective is to show the importance of MT methods combined with the effectiveness of the descriptors. Recent years have witnessed a remarkable rise in QSAR methods based on MT and its application to drug design. New methodologies have been introduced in the area such as QSAR multi-target, Markov networks or perturbation methods. Moreover, novel topological indices, such as Bourgas' descriptors and other new concepts as the derivative of a graph or cliques capable to distinguish between conformers, have also been introduced. New drugs have also been discovered, including anticonvulsants, anineoplastics, antimalarials or antiallergics, just to name a few. In the authors' opinion, MT and QSAR have moved from an attractive possibility to representing a foundation stone in the process of drug discovery.

  11. GISMO: A MATLAB toolbox for seismic research, monitoring, & education

    NASA Astrophysics Data System (ADS)

    Thompson, G.; Reyes, C. G.; Kempler, L. A.

    2017-12-01

    GISMO is an open-source MATLAB toolbox which provides an object-oriented framework to build workflows and applications that read, process, visualize and write seismic waveform, catalog and instrument response data. GISMO can retrieve data from a variety of sources (e.g. FDSN web services, Earthworm/Winston servers) and data formats (SAC, Seisan, etc.). It can handle waveform data that crosses file boundaries. All this alleviates one of the most time consuming part for scientists developing their own codes. GISMO simplifies seismic data analysis by providing a common interface for your data, regardless of its source. Several common plots are built-in to GISMO, such as record section plots, spectrograms, depth-time sections, event count per unit time, energy release per unit time, etc. Other visualizations include map views and cross-sections of hypocentral data. Several common processing methods are also included, such as an extensive set of tools for correlation analysis. Support is being added to interface GISMO with ObsPy. GISMO encourages community development of an integrated set of codes and accompanying documentation, eliminating the need for seismologists to "reinvent the wheel". By sharing code the consistency and repeatability of results can be enhanced. GISMO is hosted on GitHub with documentation both within the source code and in the project wiki. GISMO has been used at the University of South Florida and University of Alaska Fairbanks in graduate-level courses including Seismic Data Analysis, Time Series Analysis and Computational Seismology. GISMO has also been tailored to interface with the common seismic monitoring software and data formats used by volcano observatories in the US and elsewhere. As an example, toolbox training was delivered to researchers at INETER (Nicaragua). Applications built on GISMO include IceWeb (e.g. web-based spectrograms), which has been used by Alaska Volcano Observatory since 1998 and became the prototype for the USGS

  12. ReTrOS: a MATLAB toolbox for reconstructing transcriptional activity from gene and protein expression data.

    PubMed

    Minas, Giorgos; Momiji, Hiroshi; Jenkins, Dafyd J; Costa, Maria J; Rand, David A; Finkenstädt, Bärbel

    2017-06-26

    Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation.

  13. Novel fragment-based QSAR modeling and combinatorial design of pyrazole-derived CRK3 inhibitors as potent antileishmanials.

    PubMed

    Goyal, Sukriti; Dhanjal, Jaspreet K; Tyagi, Chetna; Goyal, Manisha; Grover, Abhinav

    2014-07-01

    The CRK3 cyclin-dependent kinase of Leishmania plays an important role in regulating the cell-cycle progression at the G2/M phase checkpoint transition, proliferation, and viability inside the host macrophage. In this study, a novel fragment-based QSAR model has been developed using 22 pyrazole-derived compounds exhibiting inhibitory activity against Leishmanial CRK3. Unlike other QSAR methods, this fragment-based method gives flexibility to study the relationship between molecular fragments of interest and their contribution for the variation in the biological response by evaluating cross-term fragment descriptors. Based on the fragment-based QSAR model, a combinatorial library was generated, and top two compounds were reported after predicting their activity. The QSAR model showed satisfactory statistical parameters for the data set (r(2) = 0.8752, q(2) = 0.6690, F-ratio = 30.37, and pred_r(2) = 0.8632) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution at R1 position improves the inhibitory activity, while decline in inhibitory activity was observed in presence of nitrogen at R2 position. The analysis carried out in this study provides a substantial basis for consideration of the designed pyrazole-based leads as potent antileishmanial drugs. © 2014 John Wiley & Sons A/S.

  14. Basic Radar Altimetry Toolbox: Tools and Tutorial To Use Radar Altimetry For Cryosphere

    NASA Astrophysics Data System (ADS)

    Benveniste, J. J.; Bronner, E.; Dinardo, S.; Lucas, B. M.; Rosmorduc, V.; Earith, D.

    2010-12-01

    Radar altimetry is very much a technique expanding its applications. If quite a lot of efforts have been made for oceanography users (including easy-to-use data), the use of those data for cryosphere application, especially with the new ESA CryoSat-2 mission data is still somehow tedious, especially for new Altimetry data products users. ESA and CNES thus had the Basic Radar Altimetry Toolbox developed a few years ago, and are improving and upgrading it to fit new missions and the growing number of altimetry uses. The Basic Radar Altimetry Toolbox is an "all-altimeter" collection of tools, tutorials and documents designed to facilitate the use of radar altimetry data. The software is able: - to read most distributed radar altimetry data, from ERS-1 & 2, Topex/Poseidon, Geosat Follow-on, Jason-1, Envisat, Jason- 2, CryoSat and the future Saral missions, - to perform some processing, data editing and statistic, - and to visualize the results. It can be used at several levels/several ways: - as a data reading tool, with APIs for C, Fortran, Matlab and IDL - as processing/extraction routines, through the on-line command mode - as an educational and a quick-look tool, with the graphical user interface As part of the Toolbox, a Radar Altimetry Tutorial gives general information about altimetry, the technique involved and its applications, as well as an overview of past, present and future missions, including information on how to access data and additional software and documentation. It also presents a series of data use cases, covering all uses of altimetry over ocean, cryosphere and land, showing the basic methods for some of the most frequent manners of using altimetry data. It is an opportunity to teach remote sensing with practical training. It has been available from April 2007, and had been demonstrated during training courses and scientific meetings. About 1200 people downloaded it (Summer 2010), with many "newcomers" to altimetry among them, including teachers

  15. Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.

    PubMed

    Pingaew, Ratchanok; Prachayasittikul, Veda; Worachartcheewan, Apilak; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

    2015-10-20

    A novel series of 1,4-naphthoquinones (33-44) tethered by open and closed chain sulfonamide moieties were designed, synthesized and evaluated for their cytotoxic and antimalarial activities. All quinone-sulfonamide derivatives displayed a broad spectrum of cytotoxic activities against all of the tested cancer cell lines including HuCCA-1, HepG2, A549 and MOLT-3. Most quinones (33-36 and 38-43) exerted higher anticancer activity against HepG2 cell than that of the etoposide. The open chain analogs 36 and 42 were shown to be the most potent compounds. Notably, the restricted sulfonamide analog 38 with 6,7-dimethoxy groups exhibited the most potent antimalarial activity (IC₅₀ = 2.8 μM). Quantitative structure-activity relationships (QSAR) study was performed to reveal important chemical features governing the biological activities. Five constructed QSAR models provided acceptable predictive performance (Rcv 0.5647-0.9317 and RMSEcv 0.1231-0.2825). Four additional sets of structurally modified compounds were generated in silico (34a-34d, 36a-36k, 40a-40d and 42a-42k) in which their activities were predicted using the constructed QSAR models. A comprehensive discussion of the structure-activity relationships was made and a set of promising compounds (i.e., 33, 36, 38, 42, 36d, 36f, 42e, 42g and 42f) was suggested for further development as anticancer and antimalarial agents. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  16. Streptomyces spp. in the biocatalysis toolbox.

    PubMed

    Spasic, Jelena; Mandic, Mina; Djokic, Lidija; Nikodinovic-Runic, Jasmina

    2018-04-01

    About 20,100 research publications dated 2000-2017 were recovered searching the PubMed and Web of Science databases for Streptomyces, which are the richest known source of bioactive molecules. However, these bacteria with versatile metabolism are powerful suppliers of biocatalytic tools (enzymes) for advanced biotechnological applications such as green chemical transformations and biopharmaceutical and biofuel production. The recent technological advances, especially in DNA sequencing coupled with computational tools for protein functional and structural prediction, and the improved access to microbial diversity enabled the easier access to enzymes and the ability to engineer them to suit a wider range of biotechnological processes. The major driver behind a dramatic increase in the utilization of biocatalysis is sustainable development and the shift toward bioeconomy that will, in accordance to the UN policy agenda "Bioeconomy to 2030," become a global effort in the near future. Streptomyces spp. already play a significant role among industrial microorganisms. The intention of this minireview is to highlight the presence of Streptomyces in the toolbox of biocatalysis and to give an overview of the most important advances in novel biocatalyst discovery and applications. Judging by the steady increase in a number of recent references (228 for the 2000-2017 period), it is clear that biocatalysts from Streptomyces spp. hold promises in terms of valuable properties and applicative industrial potential.

  17. Does rational selection of training and test sets improve the outcome of QSAR modeling?

    PubMed

    Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

    2012-10-22

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

  18. Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

    PubMed

    Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

    2017-02-01

    High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

  19. Wastewater Collection System Toolbox | Eliminating Sanitary ...

    EPA Pesticide Factsheets

    2017-04-10

    Communities across the United States are working to find cost-effective, long-term approaches to managing their aging wastewater infrastructure and preventing the problems that lead to sanitary sewer overflows. The Toolbox is an effort by EPA New England to provide examples of programs and educational efforts from New England and beyond.

  20. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

    PubMed

    Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

    2014-10-01

    Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

  1. DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.

    PubMed

    Soufan, Othman; Ba-Alawi, Wail; Magana-Mora, Arturo; Essack, Magbubah; Bajic, Vladimir B

    2018-06-14

    High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F 1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem .

  2. Determination of receptor-bound drug conformations by QSAR using flexible fitting to derive a molecular similarity index

    NASA Astrophysics Data System (ADS)

    Montanari, C. A.; Tute, M. S.; Beezer, A. E.; Mitchell, J. C.

    1996-02-01

    Results are presented for a QSAR analysis of bisamidines, using a similarity index as descriptor. The method allows for differences in conformation of bisamidines at the receptor site to be taken into consideration. In particular, it has been suggested by others that pentamidine binds in the minor groove of DNA in a so-called isohelical conformation, and our QSAR supports this suggestion. The molecular similarity index for comparison of molecules can be used as a parameter for correlating and hence rationalising the activity as well as suggesting the design of bioactive molecules. The studied compounds had been evaluated for potency against Leishmania mexicana amazonensis, and this potency was used as a dependent variable in a series of QSAR analyses. For the calculation of similarity indexes, each analogue was in turn superimposed on a chosen lead compound in a reference conformation, either extended or isohelical, maximising overlap and hence similarity by flexible fitting.

  3. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

    PubMed

    Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

    2013-04-01

    Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

  4. Tracking the Sources of Fecal Contaminations: an Interdisciplinary Toolbox

    NASA Astrophysics Data System (ADS)

    Jeanneau, L.; Jarde, E.; Derrien, M.; Gruau, G.; Solecki, O.; Pourcher, A.; Marti, R.; Wéry, N.; Caprais, M.; Gourmelon, M.; Mieszkin, S.; Jadas-Hécart, A.; Communal, P.

    2011-12-01

    catchment scale by analysing three rivers impacted by fecal contaminations. The development and the application of this MST toolbox have highlighted (1) the specificity of the aforementioned markers, (2) their conservative transfer from soils to rivers and (3) their difference of persistence in seawater and in freshwater. Those results provide useful data in order to identify and manage fecal contaminations of superficial waters. In the case of single source contaminations, the markers provide coherent information: (1) the bovine or porcine markers were not detected in a river impacted by a WWTP effluent; (2) the occurrence of Rum-2-Bac and the distribution of stanols indicated a bovine contamination in a river flowing through cattle pasture. In the case of multiple source contaminations, the combination of markers is necessary to identify the main sources and the statistical treatment of the distribution of stanols could provide an approximation of their proportion.

  5. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Politi, Regina; Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599; Rusyn, Ivan, E-mail: iir@unc.edu

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict bindingmore » affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  6. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

    PubMed

    Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

    2014-03-01

    We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset. The collection of frequent subgraphs (FSG) forms a dataset-specific descriptors whose values for each molecule are defined by the number of times each frequent fragment occurs in this molecule. We have employed the FSG descriptors to develop variable selection k Nearest Neighbor (kNN) QSAR models of several datasets with binary target property including Maximum Recommended Therapeutic Dose (MRTD), Salmonella Mutagenicity (Ames Genotoxicity), and P-Glycoprotein (PGP) data. Each dataset was divided into training, test, and validation sets to establish the statistical figures of merit reflecting the model validated predictive power. The classification accuracies of models for both training and test sets for all datasets exceeded 75 %, and the accuracy for the external validation sets exceeded 72 %. The model accuracies were comparable or better than those reported earlier in the literature for the same datasets. Furthermore, the use of fragment-based descriptors affords mechanistic interpretation of validated QSAR models in terms of essential chemical fragments responsible for the compounds' target property. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. The interplay between QSAR/QSPR studies and partial order ranking and formal concept analyses.

    PubMed

    Carlsen, Lars

    2009-04-17

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental - and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example.

  8. The Interplay between QSAR/QSPR Studies and Partial Order Ranking and Formal Concept Analyses

    PubMed Central

    Carlsen, Lars

    2009-01-01

    The often observed scarcity of physical-chemical and well as toxicological data hampers the assessment of potentially hazardous chemicals released to the environment. In such cases Quantitative Structure-Activity Relationships/Quantitative Structure-Property Relationships (QSAR/QSPR) constitute an obvious alternative for rapidly, effectively and inexpensively generatng missing experimental values. However, typically further treatment of the data appears necessary, e.g., to elucidate the possible relations between the single compounds as well as implications and associations between the various parameters used for the combined characterization of the compounds under investigation. In the present paper the application of QSAR/QSPR in combination with Partial Order Ranking (POR) methodologies will be reviewed and new aspects using Formal Concept Analysis (FCA) will be introduced. Where POR constitutes an attractive method for, e.g., prioritizing a series of chemical substances based on a simultaneous inclusion of a range of parameters, FCA gives important information on the implications associations between the parameters. The combined approach thus constitutes an attractive method to a preliminary assessment of the impact on environmental and human health by primary pollutants or possibly by a primary pollutant well as a possible suite of transformation subsequent products that may be both persistent in and bioaccumulating and toxic. The present review focus on the environmental – and human health impact by residuals of the rocket fuel 1,1-dimethylhydrazine (heptyl) and its transformation products as an illustrative example. PMID:19468330

  9. Development of quantitative structure-activity relationships and its application in rational drug design.

    PubMed

    Yang, Guang-Fu; Huang, Xiaoqin

    2006-01-01

    Over forty years have elapsed since Hansch and Fujita published their pioneering work of quantitative structure-activity relationships (QSAR). Following the introduction of Comparative Molecular Field Analysis (CoMFA) by Cramer in 1998, other three-dimensional QSAR methods have been developed. Currently, combination of classical QSAR and other computational techniques at three-dimensional level is of greatest interest and generally used in the process of modern drug discovery and design. During the last several decades, a number of different mythologies incorporating a range of molecular descriptors and different statistical regression ways have been proposed and successfully applied in developing of new drugs, thus QSAR method has been proven to be indispensable in not only the reliable prediction of specific properties of new compounds, but also the help to elucidate the possible molecular mechanism of the receptor-ligand interactions. Here, we review the recent developments in QSAR and their applications in rational drug design, focusing on the reasonable selection of novel molecular descriptors and the construction of predictive QSAR models by the help of advanced computational techniques.

  10. Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

    NASA Astrophysics Data System (ADS)

    Li, Peizhen; Tian, Yueli; Zhai, Honglin; Deng, Fangfang; Xie, Meihong; Zhang, Xiaoyun

    2013-11-01

    Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

  11. Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening.

    PubMed

    Al-Masri, Ihab M; Mohammad, Mohammad K; Taha, Mutasem O

    2008-11-01

    Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

  12. UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial

    NASA Technical Reports Server (NTRS)

    Kenny, Sean P.; Crespo, Luis G.; Giesy, Daniel P.

    2012-01-01

    UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering.

  13. Parameters for Pyrethroid Insecticide QSAR and PBPK/PD Models for Human Risk Assessment

    EPA Science Inventory

    This pyrethroid insecticide parameter review is an extension of our interest in developing quantitative structure–activity relationship–physiologically based pharmacokinetic/pharmacodynamic (QSAR-PBPK/PD) models for assessing health risks, which interest started with the organoph...

  14. MOFA Software for the COBRA Toolbox

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Griesemer, Marc; Navid, Ali

    MOFA-COBRA is a software code for Matlab that performs Multi-Objective Flux Analysis (MOFA), a solving of linear programming problems. Teh leading software package for conducting different types of analyses using constrain-based models is the COBRA Toolbox for Matlab. MOFA-COBRA is an added tool for COBRA that solves multi-objective problems using a novel algorithm.

  15. Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.

    PubMed

    Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz

    2012-07-27

    Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

  16. TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy

    PubMed Central

    2011-01-01

    Background Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present. Results In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected. Conclusions TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox. PMID:22098775

  17. TRENTOOL: a Matlab open source toolbox to analyse information flow in time series data with transfer entropy.

    PubMed

    Lindner, Michael; Vicente, Raul; Priesemann, Viola; Wibral, Michael

    2011-11-18

    Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present. In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected. TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.

  18. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

    PubMed Central

    Alves, Vinicius M.; Muratov, Eugene; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2015-01-01

    Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. PMID:25560673

  19. biomechZoo: An open-source toolbox for the processing, analysis, and visualization of biomechanical movement data.

    PubMed

    Dixon, Philippe C; Loh, Jonathan J; Michaud-Paquette, Yannick; Pearsall, David J

    2017-03-01

    It is common for biomechanics data sets to contain numerous dependent variables recorded over time, for many subjects, groups, and/or conditions. These data often require standard sorting, processing, and analysis operations to be performed in order to answer research questions. Visualization of these data is also crucial. This manuscript presents biomechZoo, an open-source toolbox that provides tools and graphical user interfaces to help users achieve these goals. The aims of this manuscript are to (1) introduce the main features of the toolbox, including a virtual three-dimensional environment to animate motion data (Director), a data plotting suite (Ensembler), and functions for the computation of three-dimensional lower-limb joint angles, moments, and power and (2) compare these computations to those of an existing validated system. To these ends, the steps required to process and analyze a sample data set via the toolbox are outlined. The data set comprises three-dimensional marker, ground reaction force (GRF), joint kinematic, and joint kinetic data of subjects performing straight walking and 90° turning manoeuvres. Joint kinematics and kinetics processed within the toolbox were found to be similar to outputs from a commercial system. The biomechZoo toolbox represents the work of several years and multiple contributors to provide a flexible platform to examine time-series data sets typical in the movement sciences. The toolbox has previously been used to process and analyse walking, running, and ice hockey data sets, and can integrate existing routines, such as the KineMat toolbox, for additional analyses. The toolbox can help researchers and clinicians new to programming or biomechanics to process and analyze their data through a customizable workflow, while advanced users are encouraged to contribute additional functionality to the project. Students may benefit from using biomechZoo as a learning and research tool. It is hoped that the toolbox can play a

  20. Environmental Containment Property Estimation Using QSARs in an Expert System

    DTIC Science & Technology

    1991-10-15

    economical method to estimate aqueous solubility, octanol/ water partition coefficients, vapor pressures, organic carbon, normalized soil sorption...PROPERTY ESTIMATION USING QSARs IN AN EXPERT SYSTEM William J. Doucette Mark S. Holt Doug J. Denne Joan E. McLean Utah State University Utah Water ...persistence of a chemical are aqueous solubility, octanol/ water partition coefficient, soil/ water sorption coefficient, Henry’s Law constant

  1. SacLab: A toolbox for saccade analysis to increase usability of eye tracking systems in clinical ophthalmology practice.

    PubMed

    Cercenelli, Laura; Tiberi, Guido; Corazza, Ivan; Giannaccare, Giuseppe; Fresina, Michela; Marcelli, Emanuela

    2017-01-01

    Many open source software packages have been recently developed to expand the usability of eye tracking systems to study oculomotor behavior, but none of these is specifically designed to encompass all the main functions required for creating eye tracking tests and for providing the automatic analysis of saccadic eye movements. The aim of this study is to introduce SacLab, an intuitive, freely-available MATLAB toolbox based on Graphical User Interfaces (GUIs) that we have developed to increase the usability of the ViewPoint EyeTracker (Arrington Research, Scottsdale, AZ, USA) in clinical ophthalmology practice. SacLab consists of four processing modules that enable the user to easily create visual stimuli tests (Test Designer), record saccadic eye movements (Data Recorder), analyze the recorded data to automatically extract saccadic parameters of clinical interest (Data Analyzer) and provide an aggregate analysis from multiple eye movements recordings (Saccade Analyzer), without requiring any programming effort by the user. A demo application of SacLab to carry out eye tracking tests for the analysis of horizontal saccades was reported. We tested the usability of SacLab toolbox with three ophthalmologists who had no programming experience; the ophthalmologists were briefly trained in the use of SacLab GUIs and were asked to perform the demo application. The toolbox gained an enthusiastic feedback from all the clinicians in terms of intuitiveness, ease of use and flexibility. Test creation and data processing were accomplished in 52±21s and 46±19s, respectively, using the SacLab GUIs. SacLab may represent a useful tool to ease the application of the ViewPoint EyeTracker system in clinical routine in ophthalmology. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) Users' Workshop Presentations

    NASA Technical Reports Server (NTRS)

    Litt, Jonathan S. (Compiler)

    2018-01-01

    NASA Glenn Research Center hosted a Users' Workshop on the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) on August 21, 2017. The objective of this workshop was to update the user community on the latest features of T-MATS, and to provide a forum to present work performed using T-MATS. Presentations highlighted creative applications and the development of new features and libraries, and emphasized the flexibility and simulation power of T-MATS.

  3. The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

    PubMed

    Li, Jiazhong; Gramatica, Paola

    2010-11-01

    Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common

  4. Peptide chemistry toolbox - Transforming natural peptides into peptide therapeutics.

    PubMed

    Erak, Miloš; Bellmann-Sickert, Kathrin; Els-Heindl, Sylvia; Beck-Sickinger, Annette G

    2018-06-01

    The development of solid phase peptide synthesis has released tremendous opportunities for using synthetic peptides in medicinal applications. In the last decades, peptide therapeutics became an emerging market in pharmaceutical industry. The need for synthetic strategies in order to improve peptidic properties, such as longer half-life, higher bioavailability, increased potency and efficiency is accordingly rising. In this mini-review, we present a toolbox of modifications in peptide chemistry for overcoming the main drawbacks during the transition from natural peptides to peptide therapeutics. Modifications at the level of the peptide backbone, amino acid side chains and higher orders of structures are described. Furthermore, we are discussing the future of peptide therapeutics development and their impact on the pharmaceutical market. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study.

    PubMed

    Lin, Wei; Jiang, Ruifen; Shen, Yong; Xiong, Yaxin; Hu, Sizi; Xu, Jianqiao; Ouyang, Gangfeng

    2018-04-13

    Pre-equilibrium passive sampling is a simple and promising technique for studying sampling kinetics, which is crucial to determine the distribution, transfer and fate of hydrophobic organic compounds (HOCs) in environmental water and organisms. Environmental water samples contain complex matrices that complicate the traditional calibration process for obtaining the accurate rate constants. This study proposed a QSAR model to predict the sampling rate constants of HOCs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and pesticides) in aqueous systems containing complex matrices. A homemade flow-through system was established to simulate an actual aqueous environment containing dissolved organic matter (DOM) i.e. humic acid (HA) and (2-Hydroxypropyl)-β-cyclodextrin (β-HPCD)), and to obtain the experimental rate constants. Then, a quantitative structure-activity relationship (QSAR) model using Genetic Algorithm-Multiple Linear Regression (GA-MLR) was found to correlate the experimental rate constants to the system state including physicochemical parameters of the HOCs and DOM which were calculated and selected as descriptors by Density Functional Theory (DFT) and Chem 3D. The experimental results showed that the rate constants significantly increased as the concentration of DOM increased, and the enhancement factors of 70-fold and 34-fold were observed for the HOCs in HA and β-HPCD, respectively. The established QSAR model was validated as credible (R Adj. 2 =0.862) and predictable (Q 2 =0.835) in estimating the rate constants of HOCs for complex aqueous sampling, and a probable mechanism was developed by comparison to the reported theoretical study. The present study established a QSAR model of passive sampling rate constants and calibrated the effect of DOM on the sampling kinetics. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. A combined LS-SVM & MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogs.

    PubMed

    Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Igglessi-Markopoulou, Olga; Kollias, George

    2010-05-01

    A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure (R(LOO2)) = 0.67) and validation through an external test set (R(pred2) = 0.78). The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.

  7. Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

    NASA Astrophysics Data System (ADS)

    Ragno, Rino; Ballante, Flavio; Pirolli, Adele; Wickersham, Richard B.; Patsilinakos, Alexandros; Hesse, Stéphanie; Perspicace, Enrico; Kirsch, Gilbert

    2015-08-01

    Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

  8. Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

    2001-07-01

    Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

  9. QSAR models for prediction of chromatographic behavior of homologous Fab variants.

    PubMed

    Robinson, Julie R; Karkov, Hanne S; Woo, James A; Krogh, Berit O; Cramer, Steven M

    2017-06-01

    While quantitative structure activity relationship (QSAR) models have been employed successfully for the prediction of small model protein chromatographic behavior, there have been few reports to date on the use of this methodology for larger, more complex proteins. Recently our group generated focused libraries of antibody Fab fragment variants with different combinations of surface hydrophobicities and electrostatic potentials, and demonstrated that the unique selectivities of multimodal resins can be exploited to separate these Fab variants. In this work, results from linear salt gradient experiments with these Fabs were employed to develop QSAR models for six chromatographic systems, including multimodal (Capto MMC, Nuvia cPrime, and two novel ligand prototypes), hydrophobic interaction chromatography (HIC; Capto Phenyl), and cation exchange (CEX; CM Sepharose FF) resins. The models utilized newly developed "local descriptors" to quantify changes around point mutations in the Fab libraries as well as novel cluster descriptors recently introduced by our group. Subsequent rounds of feature selection and linearized machine learning algorithms were used to generate robust, well-validated models with high training set correlations (R 2  > 0.70) that were well suited for predicting elution salt concentrations in the various systems. The developed models then were used to predict the retention of a deamidated Fab and isotype variants, with varying success. The results represent the first successful utilization of QSAR for the prediction of chromatographic behavior of complex proteins such as Fab fragments in multimodal chromatographic systems. The framework presented here can be employed to facilitate process development for the purification of biological products from product-related impurities by in silico screening of resin alternatives. Biotechnol. Bioeng. 2017;114: 1231-1240. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  10. Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors.

    PubMed

    Abuhamdah, Sawsan; Habash, Maha; Taha, Mutasem O

    2013-12-01

    Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

  11. Quantitative structure-activity relationships (QSAR) of some 2,2-diphenyl propionate (DPP) derivatives of muscarinic antagonists

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gordon, R.K.; Breuer, E.; Padilla, F.N.

    1987-05-01

    QSAR between biological activities and molecular-chemical properties were investigated to aid in designing more effective and potent antimuscarinic pharmacophores. A molecular modeling program was used to calculate geometrical and topological values of a series of DPP pharmacophores. The newly synthesized pharmacophores were tested for their antagonist activities by: (1) inhibition of (N-methyl-/sup 3/H)scopolamine binding assay to the muscarinic receptors of N4TG1 neuroblastoma cells; (2) blocking of acetylcholine-induced contraction of guinea pig ileum; and (3) inhibition of carbachol-induced ..cap alpha..-amylase release from rat pancreas. The differences in the log of these biological activities were directly and significantly related to the distancesmore » between the carbonyl oxygen of the DPP and the quaternary nitrogen of the modified pharmacophores. The biological activities, while depending on each particular assay, varied between three and four logs of activity. The charge remained the same in all the pharmacophores. There were no QSAR correlations between molecular volume, molecular connectivity, or principle moments and their antagonistic activities, although multivariate QSAR was not employed. Thus, based on distance geometry, potent muscarinic pharmacophores can be predicted.« less

  12. 3D-QSAR studies on the inhibitory activity of trimethoprim analogues against Escherichia coli dihydrofolate reductase.

    PubMed

    Vijayaraj, Ramadoss; Devi, Mekapothula Lakshmi Vasavi; Subramanian, Venkatesan; Chattaraj, Pratim Kumar

    2012-06-01

    Three-dimensional quantitative structure activity relationship (3D-QSAR) study has been carried out on the Escherichia coli DHFR inhibitors 2,4-diamino-5-(substituted-benzyl)pyrimidine derivatives to understand the structural features responsible for the improved potency. To construct highly predictive 3D-QSAR models, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were used. The predicted models show statistically significant cross-validated and non-cross-validated correlation coefficient of r2 CV and r2 nCV, respectively. The final 3D-QSAR models were validated using structurally diverse test set compounds. Analysis of the contour maps generated from CoMFA and CoMSIA methods reveals that the substitution of electronegative groups at the first and second position along with electropositive group at the third position of R2 substitution significantly increases the potency of the derivatives. The results obtained from the CoMFA and CoMSIA study delineate the substituents on the trimethoprim analogues responsible for the enhanced potency and also provide valuable directions for the design of new trimethoprim analogues with improved affinity. © 2012 John Wiley & Sons A/S.

  13. QSAR models for reproductive toxicity and endocrine disruption in regulatory use – a preliminary investigation†

    PubMed Central

    Jensen, G.E.; Niemelä, J.R.; Wedebye, E.B.; Nikolov, N.G.

    2008-01-01

    A special challenge in the new European Union chemicals legislation, Registration, Evaluation and Authorisation of Chemicals, will be the toxicological evaluation of chemicals for reproductive toxicity. Use of valid quantitative structure–activity relationships (QSARs) is a possibility under the new legislation. This article focuses on a screening exercise by use of our own and commercial QSAR models for identification of possible reproductive toxicants. Three QSAR models were used for reproductive toxicity for the endpoints teratogenic risk to humans (based on animal tests, clinical data and epidemiological human studies), dominant lethal effect in rodents (in vivo) and Drosophila melanogaster sex-linked recessive lethal effect. A structure set of 57,014 European Inventory of Existing Chemical Substances (EINECS) chemicals was screened. A total of 5240 EINECS chemicals, corresponding to 9.2%, were predicted as reproductive toxicants by one or more of the models. The chemicals predicted positive for reproductive toxicity will be submitted to the Danish Environmental Protection Agency as scientific input for a future updated advisory classification list with advisory classifications for concern for humans owing to possible developmental toxic effects: Xn (Harmful) and R63 (Possible risk of harm to the unborn child). The chemicals were also screened in three models for endocrine disruption. PMID:19061080

  14. 20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)

    EPA Science Inventory

    Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...

  15. T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides

    NASA Astrophysics Data System (ADS)

    Tian, Feifei; Zhou, Peng; Li, Zhiliang

    2007-03-01

    In this paper, a new topological descriptor T-scale is derived from principal component analysis (PCA) on the collected 67 kinds of structural and topological variables of 135 amino acids. Applying T-scale to three peptide panels as 58 angiotensin-converting enzyme (ACE) inhibitors, 20 thromboplastin inhibitors (TI) and 28 bovine lactoferricin-(17-31)-pentadecapeptides (LFB), the resulting QSAR models, constructed by partial least squares (PLS), are all superior to reference reports, with correlative coefficient r2 and cross-validated q2 of 0.845, 0.786; 0.996, 0.782 (0.988, 0.961); 0.760, 0.627, respectively.

  16. COMETS2: An advanced MATLAB toolbox for the numerical analysis of electric fields generated by transcranial direct current stimulation.

    PubMed

    Lee, Chany; Jung, Young-Jin; Lee, Sang Jun; Im, Chang-Hwan

    2017-02-01

    Since there is no way to measure electric current generated by transcranial direct current stimulation (tDCS) inside the human head through in vivo experiments, numerical analysis based on the finite element method has been widely used to estimate the electric field inside the head. In 2013, we released a MATLAB toolbox named COMETS, which has been used by a number of groups and has helped researchers to gain insight into the electric field distribution during stimulation. The aim of this study was to develop an advanced MATLAB toolbox, named COMETS2, for the numerical analysis of the electric field generated by tDCS. COMETS2 can generate any sizes of rectangular pad electrodes on any positions on the scalp surface. To reduce the large computational burden when repeatedly testing multiple electrode locations and sizes, a new technique to decompose the global stiffness matrix was proposed. As examples of potential applications, we observed the effects of sizes and displacements of electrodes on the results of electric field analysis. The proposed mesh decomposition method significantly enhanced the overall computational efficiency. We implemented an automatic electrode modeler for the first time, and proposed a new technique to enhance the computational efficiency. In this paper, an efficient toolbox for tDCS analysis is introduced (freely available at http://www.cometstool.com). It is expected that COMETS2 will be a useful toolbox for researchers who want to benefit from the numerical analysis of electric fields generated by tDCS. Copyright © 2016. Published by Elsevier B.V.

  17. 4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

    PubMed Central

    Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

    2008-01-01

    Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

  18. Azolium analogues as CDK4 inhibitors: Pharmacophore modeling, 3D QSAR study and new lead drug discovery

    NASA Astrophysics Data System (ADS)

    Rondla, Rohini; Padma Rao, Lavanya Souda; Ramatenki, Vishwanath; Vadija, Rajender; Mukkera, Thirupathi; Potlapally, Sarita Rajender; Vuruputuri, Uma

    2017-04-01

    The cyclin-dependent kinase 4 (CDK4) enzyme is a key regulator in cell cycle G1 phase progression. It is often overexpressed in variety of cancer cells, which makes it an attractive therapeutic target for cancer treatment. A number of chemical scaffolds have been reported as CDK4 inhibitors in the literature, and in particular azolium scaffolds as potential inhibitors. Here, a ligand based pharmacophore modeling and an atom based 3D-QSAR analyses for a series of azolium based CDK4 inhibitors are presented. A five point pharmacophore hypothesis, i.e. APRRR with one H-bond acceptor (A), one positive cationic feature (P) and three ring aromatic sites (R) is developed, which yielded an atom based 3D-QSAR model that shows an excellent correlation coefficient value- R2 = 0.93, fisher ratio- F = 207, along with good predictive ability- Q2 = 0.79, and Pearson R value = 0.89. The visual inspection of the 3D-QSAR model, with the most active and the least active ligands, demonstrates the favorable and unfavorable structural regions for the activity towards CDK4. The roles of positively charged nitrogen, the steric effect, ligand flexibility, and the substituents on the activity are in good agreement with the previously reported experimental results. The generated 3D QSAR model is further applied as query for a 3D database screening, which identifies 23 lead drug candidates with good predicted activities and diverse scaffolds. The ADME analysis reveals that, the pharmacokinetic parameters of all the identified new leads are within the acceptable range.

  19. Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredients.

    PubMed

    Hisaki, Tomoka; Aiba Née Kaneko, Maki; Yamaguchi, Masahiko; Sasa, Hitoshi; Kouzuki, Hirokazu

    2015-04-01

    Use of laboratory animals for systemic toxicity testing is subject to strong ethical and regulatory constraints, but few alternatives are yet available. One possible approach to predict systemic toxicity of chemicals in the absence of experimental data is quantitative structure-activity relationship (QSAR) analysis. Here, we present QSAR models for prediction of maximum "no observed effect level" (NOEL) for repeated-dose, developmental and reproductive toxicities. NOEL values of 421 chemicals for repeated-dose toxicity, 315 for reproductive toxicity, and 156 for developmental toxicity were collected from Japan Existing Chemical Data Base (JECDB). Descriptors to predict toxicity were selected based on molecular orbital (MO) calculations, and QSAR models employing multiple independent descriptors as the input layer of an artificial neural network (ANN) were constructed to predict NOEL values. Robustness of the models was indicated by the root-mean-square (RMS) errors after 10-fold cross-validation (0.529 for repeated-dose, 0.508 for reproductive, and 0.558 for developmental toxicity). Evaluation of the models in terms of the percentages of predicted NOELs falling within factors of 2, 5 and 10 of the in-vivo-determined NOELs suggested that the model is applicable to both general chemicals and the subset of chemicals listed in International Nomenclature of Cosmetic Ingredients (INCI). Our results indicate that ANN models using in silico parameters have useful predictive performance, and should contribute to integrated risk assessment of systemic toxicity using a weight-of-evidence approach. Availability of predicted NOELs will allow calculation of the margin of safety, as recommended by the Scientific Committee on Consumer Safety (SCCS).

  20. Experimental verification, and domain definition, of structural alerts for protein binding: epoxides, lactones, nitroso, nitros, aldehydes and ketones.

    PubMed

    Nelms, M D; Cronin, M T D; Schultz, T W; Enoch, S J

    2013-01-01

    This study outlines how a combination of in chemico and Tetrahymena pyriformis data can be used to define the applicability domain of selected structural alerts within the profilers of the OECD QSAR Toolbox. Thirty-three chemicals were profiled using the OECD and OASIS profilers, enabling the applicability domain of six structural alerts to be defined, the alerts being: epoxides, lactones, nitrosos, nitros, aldehydes and ketones. Analysis of the experimental data showed the applicability domains for the epoxide, nitroso, aldehyde and ketone structural alerts to be well defined. In contrast, the data showed the applicability domains for the lactone and nitro structural alerts needed modifying. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in the chemical category in predictive and regulatory toxicology. This study highlights the importance of utilizing multiple profilers in category formation.

  1. FALCON: a toolbox for the fast contextualization of logical networks

    PubMed Central

    De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

    2017-01-01

    Abstract Motivation Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. Results We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. Availability and implementation FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. Contact thomas.sauter@uni.lu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28673016

  2. FALCON: a toolbox for the fast contextualization of logical networks.

    PubMed

    De Landtsheer, Sébastien; Trairatphisan, Panuwat; Lucarelli, Philippe; Sauter, Thomas

    2017-11-01

    Mathematical modelling of regulatory networks allows for the discovery of knowledge at the system level. However, existing modelling tools are often computation-heavy and do not offer intuitive ways to explore the model, to test hypotheses or to interpret the results biologically. We have developed a computational approach to contextualize logical models of regulatory networks with biological measurements based on a probabilistic description of rule-based interactions between the different molecules. Here, we propose a Matlab toolbox, FALCON, to automatically and efficiently build and contextualize networks, which includes a pipeline for conducting parameter analysis, knockouts and easy and fast model investigation. The contextualized models could then provide qualitative and quantitative information about the network and suggest hypotheses about biological processes. FALCON is freely available for non-commercial users on GitHub under the GPLv3 licence. The toolbox, installation instructions, full documentation and test datasets are available at https://github.com/sysbiolux/FALCON. FALCON runs under Matlab (MathWorks) and requires the Optimization Toolbox. thomas.sauter@uni.lu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  3. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

    PubMed

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2018-02-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  4. A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

    NASA Astrophysics Data System (ADS)

    Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

    2018-02-01

    Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

  5. The panacea toolbox of a PhD biomedical student.

    PubMed

    Skaik, Younis

    2014-01-01

    Doing a PhD (doctor of philosophy) for the sake of contribution to knowledge should give the student an immense enthusiasm through the PhD period. It is the time in one's life that one spends to "hit the nail on the head" in a specific area and topic of interest. A PhD consists mostly of hard work and tenacity; however, luck and genius might also play a little role. You can pass all PhD phases without having both luck and genius. The PhD student should have pre-PhD and PhD toolboxes, which are "sine quibus non" for getting successfully a PhD degree. In this manuscript, the toolboxes of the PhD student are discussed.

  6. A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

    NASA Astrophysics Data System (ADS)

    Belvisi, Laura; Bravi, Gianpaolo; Catalano, Giovanna; Mabilia, Massimo; Salimbeni, Aldo; Scolastico, Carlo

    1996-12-01

    A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.

  7. NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis.

    PubMed

    Zhang, Bo; Dai, Ji; Zhang, Tao

    2017-11-13

    In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful. Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level. With the multitude of general-purpose functions provided

  8. The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

    PubMed Central

    Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

    2015-01-01

    The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

  9. TopoToolbox: using sensor topography to calculate psychologically meaningful measures from event-related EEG/MEG.

    PubMed

    Tian, Xing; Poeppel, David; Huber, David E

    2011-01-01

    The open-source toolbox "TopoToolbox" is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).

  10. Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

    PubMed

    Cruz-Monteagudo, Maykel; Borges, Fernanda; Cordeiro, M Natália D S; Cagide Fajin, J Luis; Morell, Carlos; Ruiz, Reinaldo Molina; Cañizares-Carmenate, Yudith; Dominguez, Elena Rosa

    2008-01-01

    Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

  11. Wavefront Control Toolbox for James Webb Space Telescope Testbed

    NASA Technical Reports Server (NTRS)

    Shiri, Ron; Aronstein, David L.; Smith, Jeffery Scott; Dean, Bruce H.; Sabatke, Erin

    2007-01-01

    We have developed a Matlab toolbox for wavefront control of optical systems. We have applied this toolbox to the optical models of James Webb Space Telescope (JWST) in general and to the JWST Testbed Telescope (TBT) in particular, implementing both unconstrained and constrained wavefront optimization to correct for possible misalignments present on the segmented primary mirror or the monolithic secondary mirror. The optical models implemented in Zemax optical design program and information is exchanged between Matlab and Zemax via the Dynamic Data Exchange (DDE) interface. The model configuration is managed using the XML protocol. The optimization algorithm uses influence functions for each adjustable degree of freedom of the optical mode. The iterative and non-iterative algorithms have been developed to converge to a local minimum of the root-mean-square (rms) of wavefront error using singular value decomposition technique of the control matrix of influence functions. The toolkit is highly modular and allows the user to choose control strategies for the degrees of freedom to be adjusted on a given iteration and wavefront convergence criterion. As the influence functions are nonlinear over the control parameter space, the toolkit also allows for trade-offs between frequency of updating the local influence functions and execution speed. The functionality of the toolbox and the validity of the underlying algorithms have been verified through extensive simulations.

  12. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research

    PubMed Central

    Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

    2010-01-01

    Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all

  13. MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation.

    PubMed

    Mantoan, Alice; Pizzolato, Claudio; Sartori, Massimo; Sawacha, Zimi; Cobelli, Claudio; Reggiani, Monica

    2015-01-01

    Neuromusculoskeletal modeling and simulation enable investigation of the neuromusculoskeletal system and its role in human movement dynamics. These methods are progressively introduced into daily clinical practice. However, a major factor limiting this translation is the lack of robust tools for the pre-processing of experimental movement data for their use in neuromusculoskeletal modeling software. This paper presents MOtoNMS (matlab MOtion data elaboration TOolbox for NeuroMusculoSkeletal applications), a toolbox freely available to the community, that aims to fill this lack. MOtoNMS processes experimental data from different motion analysis devices and generates input data for neuromusculoskeletal modeling and simulation software, such as OpenSim and CEINMS (Calibrated EMG-Informed NMS Modelling Toolbox). MOtoNMS implements commonly required processing steps and its generic architecture simplifies the integration of new user-defined processing components. MOtoNMS allows users to setup their laboratory configurations and processing procedures through user-friendly graphical interfaces, without requiring advanced computer skills. Finally, configuration choices can be stored enabling the full reproduction of the processing steps. MOtoNMS is released under GNU General Public License and it is available at the SimTK website and from the GitHub repository. Motion data collected at four institutions demonstrate that, despite differences in laboratory instrumentation and procedures, MOtoNMS succeeds in processing data and producing consistent inputs for OpenSim and CEINMS. MOtoNMS fills the gap between motion analysis and neuromusculoskeletal modeling and simulation. Its support to several devices, a complete implementation of the pre-processing procedures, its simple extensibility, the available user interfaces, and its free availability can boost the translation of neuromusculoskeletal methods in daily and clinical practice.

  14. Sentinel-3 SAR Altimetry Toolbox - Scientific Exploitation of Operational Missions (SEOM) Program Element

    NASA Astrophysics Data System (ADS)

    Benveniste, Jérôme; Lucas, Bruno; Dinardo, Salvatore

    2014-05-01

    CDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, showing its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel-3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the Sentinel-3 SAR Altimetry Toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific "use-cases" for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The ITT is expected to be launched in Q1 2014 and have the 1st version available before the launch of Sentinel-3.

  15. Sentinel-3 SAR Altimetry Toolbox - Scientific Exploitation of Operational Missions (SEOM) Program Element

    NASA Astrophysics Data System (ADS)

    Benveniste, Jérôme; Dinardo, Salvatore; Lucas, Bruno Manuel

    netCDF, ASCII text files, KML (Google Earth) and raster images (JPEG, PNG, etc.). Several kinds of computations can be done within BRAT involving combinations of data fields that the user can save for posterior reuse or using the already embedded formulas that include the standard oceanographic altimetry formulas. The Radar Altimeter Tutorial, that contains a strong introduction to altimetry, showing its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also “use cases”, with step-by-step examples, on how to use the toolbox in the different contexts. The Sentinel-3 SAR Altimetry Toolbox shall benefit from the current BRAT version. While developing the Sentinel-3 SAR Altimetry Toolbox we will revamp of the Graphical User Interface and provide, among other enhancements, support for reading the upcoming S3 datasets and specific “use-cases” for SAR altimetry in order to train the users and make them aware of the great potential of SAR altimetry for coastal and inland applications. As for any open source framework, contributions from users having developed their own functions are welcome. The ITT is expected to be launched in Q1 2014 and have the 1st version available before the launch of Sentinel-3.

  16. ObsPy: A Python toolbox for seismology - Sustainability, New Features, and Applications

    NASA Astrophysics Data System (ADS)

    Krischer, L.; Megies, T.; Sales de Andrade, E.; Barsch, R.; MacCarthy, J.

    2016-12-01

    ObsPy (https://www.obspy.org) is a community-driven, open-source project dedicated to offer a bridge for seismology into the scientific Python ecosystem. Amongst other things, it provides Read and write support for essentially every commonly used data format in seismology with a unified interface. This includes waveform data as well as station and event meta information. A signal processing toolbox tuned to the specific needs of seismologists. Integrated access to the largest data centers, web services, and databases. Wrappers around third party codes like libmseed and evalresp. Using ObsPy enables users to take advantage of the vast scientific ecosystem that has developed around Python. In contrast to many other programming languages and tools, Python is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often must be translated to stable and production ready environments, especially in the age of big data. ObsPy has seen constant development for more than six years and enjoys a large rate of adoption in the seismological community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it. We will present a short overview of the capabilities of ObsPy and point out several representative use cases and more specialized software built around ObsPy. Additionally we will discuss new and upcoming features, as well as the sustainability of open-source scientific software.

  17. RESPONSE PROTOCOL TOOLBOX: PLANNING FOR AND RESPONDING TO DRINKING WATER CONTAMINATION THREATS AND INCIDENTS. OVERVIEW AND APPLICATION. INTERIM FINAL - DECEMBER 2003

    EPA Science Inventory

    The interim final Response Protocol Toolbox: Planning for and Responding to Contamination Threats to Drinking Water Systems is designed to help the water sector effectively and appropriately respond to intentional contamination threats and incidents. It was produced by EPA, buil...

  18. A portable toolbox to monitor and evaluate signal operations.

    DOT National Transportation Integrated Search

    2011-10-01

    Researchers from the Texas Transportation Institute developed a portable tool consisting of a fieldhardened : computer interfacing with the traffic signal cabinet through special enhanced Bus Interface Units. : The toolbox consisted of a monitoring t...

  19. QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

    PubMed Central

    Alam, Sarfaraz; Khan, Feroz

    2014-01-01

    Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets. PMID:24516330

  20. Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

    PubMed

    Speck-Planche, Alejandro; Kleandrova, Valeria V; Luan, Feng; Cordeiro, M Natália D S

    2012-08-01

    The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents. Copyright © 2012 Elsevier Ltd. All rights reserved.

  1. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1).

    PubMed

    Begum, S; Achary, P Ganga Raju

    2015-01-01

    Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.

  2. Molecular Docking, Pharmacophore, and 3D-QSAR Approach: Can Adenine Derivatives Exhibit Significant Inhibitor Towards Ebola Virus?

    PubMed Central

    Rai, Amit; Aboumanei, Mohamed H.; Verma, Suraj P.; Kumar, Sachidanand; Raj, Vinit

    2017-01-01

    Introduction: Ebola Virus Disease (EVD) is caused by Ebola virus, which is often accompanied by fatal hemorrhagic fever upon infection in humans. This virus has caused the majority of deaths in human. There are no proper vaccinations and medications available for EVD. It is pivoting the attraction of scientist to develop the potent vaccination or novel lead to inhibit Ebola virus. Methods & Materials: In the present study, we developed 3D-QSAR and the pharmacophoric model from the previous reported potent compounds for the Ebola virus. Results & Discussion: Results & Discussion: The pharmacophoric model AAAP.116 was generated with better survival value and selectivity. Moreover, the 3D-QSAR model also showed the best r2 value 0.99 using PLS factor. Thereby, we found the higher F value, which demonstrated the statistical significance of both the models. Furthermore, homological modeling and molecular docking study were performed to analyze the affinity of the potent lead. This showed the best binding energy and bond formation with targeted protein. Conclusion: Finally, all the results of this study concluded that 3D-QSAR and Pharmacophore models may be helpful to search potent lead for EVD treatment in future. PMID:29387271

  3. Impact-oriented steering--the concept of NGO-IDEAs 'impact toolbox'.

    PubMed

    2008-03-01

    The NGO-IDEAs 'Impact Toolbox' has been developed with a group of NGOs all of which are active in the area of saving and credit in South India. This compilation of methods to apply in impact-oriented steering was devised by the executive staff of the Indian partner NGOs, also known as the Resource Persons, in 2006 and tested from late 2006 to early 2007. At first glance, the approach may appear to be highly specialised and difficult to transfer. However, in fact it follows principles that can be adapted for several NGOs in other countries and in other sectors. The following article presents the concept of the NGO-IDEAs 'Impact Toolbox'.

  4. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

    PubMed Central

    Alves, Vinicius M.; Capuzzi, Stephen J.; Muratov, Eugene; Braga, Rodolpho C.; Thornton, Thomas; Fourches, Denis; Strickland, Judy; Kleinstreuer, Nicole; Andrade, Carolina H.; Tropsha, Alexander

    2016-01-01

    Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential. PMID:28630595

  5. Critical body residues linked to octanol-water partitioning, organism composition, and LC50 QSARs: meta-analysis and model.

    PubMed

    Hendriks, A Jan; Traas, Theo P; Huijbregts, Mark A J

    2005-05-01

    To protect thousands of species from thousands of chemicals released in the environment, various risk assessment tools have been developed. Here, we link quantitative structure-activity relationships (QSARs) for response concentrations in water (LC50) to critical concentrations in organisms (C50) by a model for accumulation in lipid or non-lipid phases versus water Kpw. The model indicates that affinity for neutral body components such as storage fat yields steep Kpw-Kow relationships, whereas slopes for accumulation in polar phases such as proteins are gentle. This pattern is confirmed by LC50 QSARs for different modes of action, such as neutral versus polar narcotics and organochlorine versus organophosphor insecticides. LC50 QSARs were all between 0.00002 and 0.2Kow(-1). After calibrating the model with the intercepts and, for the first time also, with the slopes of the LC50 QSARs, critical concentrations in organisms C50 are calculated and compared to an independent validation data set. About 60% of the variability in lethal body burdens C50 is explained by the model. Explanations for differences between estimated and measured levels for 11 modes of action are discussed. In particular, relationships between the critical concentrations in organisms C50 and chemical (Kow) or species (lipid content) characteristics are specified and tested. The analysis combines different models proposed before and provides a substantial extension of the data set in comparison to previous work. Moreover, the concept is applied to species (e.g., plants, lean animals) and substances (e.g., specific modes of action) that were scarcely studied quantitatively so far.

  6. USE OF INTERSPECIES CORRELATION ESTIMATIONS TO PREDICT HC5'S BASED ON QSAR

    EPA Science Inventory

    Dyer, S.D., S. Belanger, J. Chaney, D. Versteeg and F. Mayer. In press. Use of Interspecies Correlation Estimations to predict HC5's Based on QSARs (Abstract). To be presented at the SETAC Europe 14th Annual Meeting: Environmental Science Solution: A Pan-European Perspective, 18-...

  7. Importance of Kier-Hall topological indices in the QSAR of anticancer drug design.

    PubMed

    Nandi, Sisir; Bagchi, Manish C

    2012-06-01

    , the structural model of an assembled entity (e.g. a molecule consisting of atoms) may be defined as the pattern of relationship among its parts as distinct from the values associated with them. Constitutional formulae of molecules are graphs where vertices represent the set of atoms and edges represent chemical bonds. The pattern of connectedness of atoms in a molecule is preserved by constitutional graphs. A graph (more correctly a non-directed graph) G = [V, E] consists of a finite non-empty set V of points together with a prescribed set E of unordered pairs of distinct points of V. Thus the mathematical characterization of structures represents structural invariants having successful applications in chemical documentation, characterization of molecular branching, enumeration of molecular constitutional associated with a particular empirical formula, calculation of quantum chemical parameters for the generation of quantitative structure-property-activity correlations. Kier developed a number of structural invariants which are now-a-days called as topological indices with wide range of practical applications for QSAR and drug design. The present paper is restricted to the review of Kier-Hall topological indices for QSAR and anticancer drug design for 2,5-bis(1-aziridinyl) 1,4-benzoquinone (BABQ), pyridopyrimidine, 4-anilinoquinazoline and 2-Phenylindoles compounds utilizing various statistical multivariate regression analyses.

  8. Efficient dynamic molecular simulation using QSAR model to know inhibition activity in breast cancer medicine

    NASA Astrophysics Data System (ADS)

    Zharifah, A.; Kusumowardani, E.; Saputro, A.; Sarwinda, D.

    2017-07-01

    According to data from GLOBOCAN (IARC) at 2012, breast cancer was the highest rated of new cancer case by 43.3 % (after controlled by age), with mortality rated as high as 12.9 %. Oncology is a major field which focusing on improving the development of drug and therapeutics cancer in pharmaceutical and biotechnology companies. Nowadays, many researchers lead to computational chemistry and bioinformatic for pharmacophore generation. A pharmacophore describes as a group of atoms in the molecule which is considered to be responsible for a pharmacological action. Prediction of biological function from chemical structure in silico modeling reduces the use of chemical reagents so the risk of environmental pollution decreased. In this research, we proposed QSAR model to analyze the composition of cancer drugs which assumed to be homogenous in character and treatment. Atomic interactions which analyzed are learned through parameters such as log p as descriptors hydrophobic, n_poinas descriptor contour strength and molecular structure, and also various concentrations inhibitor (micromolar and nanomolar) from NCBI drugs bank. The differences inhibitor activity was observed by the presence of IC 50 residues value from inhibitor substances at various concentration. Then, we got a general overview of the state of safety for drug stability seen from its IC 50 value. In our study, we also compared between micromolar and nanomolar inhibitor effect from QSAR model results. The QSAR model analysis shows that the drug concentration with nanomolar is better than micromolar, related with the content of inhibitor substances concentration. This QSAR model got the equation: Log 1/IC50 = (0.284) (±0.195) logP + (0.02) (±0.012) n_poin + (-0.005) (±0.083) Inhibition10.2nanoM + (0.1) (±0.079) Inhibition30.5nanoM + (-0.016) (±0.045) Inhibition91.5nanoM + (-2.572) (±1.570) (n = 13; r = 0.813; r2 = 0.660; s = 0.764; F = 2.720; q2 = 0.660).

  9. The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

    NASA Astrophysics Data System (ADS)

    Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

    2014-10-01

    Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

  10. Cross-species 3D virtual reality toolbox for visual and cognitive experiments.

    PubMed

    Doucet, Guillaume; Gulli, Roberto A; Martinez-Trujillo, Julio C

    2016-06-15

    Although simplified visual stimuli, such as dots or gratings presented on homogeneous backgrounds, provide strict control over the stimulus parameters during visual experiments, they fail to approximate visual stimulation in natural conditions. Adoption of virtual reality (VR) in neuroscience research has been proposed to circumvent this problem, by combining strict control of experimental variables and behavioral monitoring within complex and realistic environments. We have created a VR toolbox that maximizes experimental flexibility while minimizing implementation costs. A free VR engine (Unreal 3) has been customized to interface with any control software via text commands, allowing seamless introduction into pre-existing laboratory data acquisition frameworks. Furthermore, control functions are provided for the two most common programming languages used in visual neuroscience: Matlab and Python. The toolbox offers milliseconds time resolution necessary for electrophysiological recordings and is flexible enough to support cross-species usage across a wide range of paradigms. Unlike previously proposed VR solutions whose implementation is complex and time-consuming, our toolbox requires minimal customization or technical expertise to interface with pre-existing data acquisition frameworks as it relies on already familiar programming environments. Moreover, as it is compatible with a variety of display and input devices, identical VR testing paradigms can be used across species, from rodents to humans. This toolbox facilitates the addition of VR capabilities to any laboratory without perturbing pre-existing data acquisition frameworks, or requiring any major hardware changes. Copyright © 2016 Z. All rights reserved.

  11. Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

    PubMed Central

    Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

    2011-01-01

    Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

  12. A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

    PubMed

    Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

    2015-05-30

    A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

  13. Multirate Flutter Suppression System Design for the Benchmark Active Controls Technology Wing. Part 2; Methodology Application Software Toolbox

    NASA Technical Reports Server (NTRS)

    Mason, Gregory S.; Berg, Martin C.; Mukhopadhyay, Vivek

    2002-01-01

    To study the effectiveness of various control system design methodologies, the NASA Langley Research Center initiated the Benchmark Active Controls Project. In this project, the various methodologies were applied to design a flutter suppression system for the Benchmark Active Controls Technology (BACT) Wing. This report describes the user's manual and software toolbox developed at the University of Washington to design a multirate flutter suppression control law for the BACT wing.

  14. The BRAT and GUT Couple: Broadview Radar Altimetry and GOCE User Toolboxes

    NASA Astrophysics Data System (ADS)

    Benveniste, J.; Restano, M.; Ambrózio, A.

    2017-12-01

    The Broadview Radar Altimetry Toolbox (BRAT) is a collection of tools designed to facilitate the processing of radar altimetry data from previous and current altimetry missions, including Sentinel-3A L1 and L2 products. A tutorial is included providing plenty of use cases. BRAT's next release (4.2.0) is planned for October 2017. Based on the community feedback, the front-end has been further improved and simplified whereas the capability to use BRAT in conjunction with MATLAB/IDL or C/C++/Python/Fortran, allowing users to obtain desired data bypassing the data-formatting hassle, remains unchanged. Several kinds of computations can be done within BRAT involving the combination of data fields, that can be saved for future uses, either by using embedded formulas including those from oceanographic altimetry, or by implementing ad-hoc Python modules created by users to meet their needs. BRAT can also be used to quickly visualise data, or to translate data into other formats, e.g. from NetCDF to raster images. The GOCE User Toolbox (GUT) is a compilation of tools for the use and the analysis of GOCE gravity field models. It facilitates using, viewing and post-processing GOCE L2 data and allows gravity field data, in conjunction and consistently with any other auxiliary data set, to be pre-processed by beginners in gravity field processing, for oceanographic and hydrologic as well as for solid earth applications at both regional and global scales. Hence, GUT facilitates the extensive use of data acquired during GRACE and GOCE missions. In the current 3.1 version, GUT has been outfitted with a graphical user interface allowing users to visually program data processing workflows. Further enhancements aiming at facilitating the use of gradients, the anisotropic diffusive filtering, and the computation of Bouguer and isostatic gravity anomalies have been introduced. Packaged with GUT is also GUT's Variance-Covariance Matrix tool (VCM). BRAT and GUT toolboxes can be freely

  15. III. NIH Toolbox Cognition Battery (CB): measuring episodic memory.

    PubMed

    Bauer, Patricia J; Dikmen, Sureyya S; Heaton, Robert K; Mungas, Dan; Slotkin, Jerry; Beaumont, Jennifer L

    2013-08-01

    One of the most significant domains of cognition is episodic memory, which allows for rapid acquisition and long-term storage of new information. For purposes of the NIH Toolbox, we devised a new test of episodic memory. The nonverbal NIH Toolbox Picture Sequence Memory Test (TPSMT) requires participants to reproduce the order of an arbitrarily ordered sequence of pictures presented on a computer. To adjust for ability, sequence length varies from 6 to 15 pictures. Multiple trials are administered to increase reliability. Pediatric data from the validation study revealed the TPSMT to be sensitive to age-related changes. The task also has high test-retest reliability and promising construct validity. Steps to further increase the sensitivity of the instrument to individual and age-related variability are described. © 2013 The Society for Research in Child Development, Inc.

  16. Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

    NASA Astrophysics Data System (ADS)

    Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

    2008-06-01

    In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

  17. Synthesis and quantitative structure activity relationship (QSAR) of arylidene (benzimidazol-1-yl)acetohydrazones as potential antibacterial agents.

    PubMed

    El-Kilany, Yeldez; Nahas, Nariman M; Al-Ghamdi, Mariam A; Badawy, Mohamed E I; El Ashry, El Sayed H

    2015-01-01

    Ethyl (benzimidazol-1-yl)acetate was subjected to hydrazinolysis with hydrazine hydrate to give (benzimidazol-1-yl)acetohydrazide. The latter was reacted with various aromatic aldehydes to give the respective arylidene (1H-benzimidazol-1-yl)acetohydrazones. Solutions of the prepared hydrazones were found to contain two geometric isomers. Similarly (2-methyl-benzimidazol-1-yl)acetohydrazide was reacted with various aldehydes to give the corresponding hydrazones. The antibacterial activity was evaluated in vitro by minimum inhibitory concentration (MIC) against Agrobacterium tumefaciens (A. tumefaciens), Erwinia carotovora (E. carotovora), Corynebacterium fascians (C. fascians) and Pseudomonas solanacearum (P. solanacearum). MIC result demonstrated that salicylaldehyde(1H-benzimidazol-1-yl)acetohydrazone (4) was the most active compound (MIC = 20, 35, 25 and 30 mg/L against A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively). Quantitative structure activity relationship (QSAR) investigation using Hansch analysis was applied to find out the correlation between antibacterial activity and physicochemical properties. Various physicochemical descriptors and experimentally determined MIC values for different microorganisms were used as independent and dependent variables, respectively. pMICs of the compounds exhibited good correlation (r = 0.983, 0.914, 0.960 and 0.958 for A. tumefaciens, C. fascians, E. carotovora and P. solanacearum, respectively) with the prediction made by the model. QSAR study revealed that the hydrophobic parameter (ClogP), the aqueous solubility (LogS), calculated molar refractivity, topological polar surface area and hydrogen bond acceptor were found to have overall significant correlation with antibacterial activity. The statistical results of training set, correlation coefficient (r and r (2)), the ratio between regression and residual variances (f, Fisher's statistic), the standard error of estimates and

  18. Testing Adaptive Toolbox Models: A Bayesian Hierarchical Approach

    ERIC Educational Resources Information Center

    Scheibehenne, Benjamin; Rieskamp, Jorg; Wagenmakers, Eric-Jan

    2013-01-01

    Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox…

  19. 3D QSAR models built on structure-based alignments of Abl tyrosine kinase inhibitors.

    PubMed

    Falchi, Federico; Manetti, Fabrizio; Carraro, Fabio; Naldini, Antonella; Maga, Giovanni; Crespan, Emmanuele; Schenone, Silvia; Bruno, Olga; Brullo, Chiara; Botta, Maurizio

    2009-06-01

    Quality QSAR: A combination of docking calculations and a statistical approach toward Abl inhibitors resulted in a 3D QSAR model, the analysis of which led to the identification of ligand portions important for affinity. New compounds designed on the basis of the model were found to have very good affinity for the target, providing further validation of the model itself.The X-ray crystallographic coordinates of the Abl tyrosine kinase domain in its active, inactive, and Src-like inactive conformations were used as targets to simulate the binding mode of a large series of pyrazolo[3,4-d]pyrimidines (known Abl inhibitors) by means of GOLD software. Receptor-based alignments provided by molecular docking calculations were submitted to a GRID-GOLPE protocol to generate 3D QSAR models. Analysis of the results showed that the models based on the inactive and Src-like inactive conformations had very poor statistical parameters, whereas the sole model based on the active conformation of Abl was characterized by significant internal and external predictive ability. Subsequent analysis of GOLPE PLS pseudo-coefficient contour plots of this model gave us a better understanding of the relationships between structure and affinity, providing suggestions for the next optimization process. On the basis of these results, new compounds were designed according to the hydrophobic and hydrogen bond donor and acceptor contours, and were found to have improved enzymatic and cellular activity with respect to parent compounds. Additional biological assays confirmed the important role of the selected compounds as inhibitors of cell proliferation in leukemia cells.

  20. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY

    EPA Science Inventory

    The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes bey...

  1. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  2. DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

    NASA Astrophysics Data System (ADS)

    D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco P.; Pasquariello, Guido

    2018-03-01

    High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

  3. Expanding the metabolic engineering toolbox with directed evolution.

    PubMed

    Abatemarco, Joseph; Hill, Andrew; Alper, Hal S

    2013-12-01

    Cellular systems can be engineered into factories that produce high-value chemicals from renewable feedstock. Such an approach requires an expanded toolbox for metabolic engineering. Recently, protein engineering and directed evolution strategies have started to play a growing and critical role within metabolic engineering. This review focuses on the various ways in which directed evolution can be applied in conjunction with metabolic engineering to improve product yields. Specifically, we discuss the application of directed evolution on both catalytic and non-catalytic traits of enzymes, on regulatory elements, and on whole genomes in a metabolic engineering context. We demonstrate how the goals of metabolic pathway engineering can be achieved in part through evolving cellular parts as opposed to traditional approaches that rely on gene overexpression and deletion. Finally, we discuss the current limitations in screening technology that hinder the full implementation of a metabolic pathway-directed evolution approach. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?

    EPA Science Inventory

    Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external dataset, the best way to validate the predictive ability of a model is to perform its s...

  5. Quantitative structure activity relationship (QSAR) of piperine analogs for bacterial NorA efflux pump inhibitors.

    PubMed

    Nargotra, Amit; Sharma, Sujata; Koul, Jawahir Lal; Sangwan, Pyare Lal; Khan, Inshad Ali; Kumar, Ashwani; Taneja, Subhash Chander; Koul, Surrinder

    2009-10-01

    Quantitative structure activity relationship (QSAR) analysis of piperine analogs as inhibitors of efflux pump NorA from Staphylococcus aureus has been performed in order to obtain a highly accurate model enabling prediction of inhibition of S. aureus NorA of new chemical entities from natural sources as well as synthetic ones. Algorithm based on genetic function approximation method of variable selection in Cerius2 was used to generate the model. Among several types of descriptors viz., topological, spatial, thermodynamic, information content and E-state indices that were considered in generating the QSAR model, three descriptors such as partial negative surface area of the compounds, area of the molecular shadow in the XZ plane and heat of formation of the molecules resulted in a statistically significant model with r(2)=0.962 and cross-validation parameter q(2)=0.917. The validation of the QSAR models was done by cross-validation, leave-25%-out and external test set prediction. The theoretical approach indicates that the increase in the exposed partial negative surface area increases the inhibitory activity of the compound against NorA whereas the area of the molecular shadow in the XZ plane is inversely proportional to the inhibitory activity. This model also explains the relationship of the heat of formation of the compound with the inhibitory activity. The model is not only able to predict the activity of new compounds but also explains the important regions in the molecules in quantitative manner.

  6. Quantitative Structure--Activity Relationship (QSAR) for the Oxidation of Trace Organic Contaminants by Sulfate Radical.

    PubMed

    Xiao, Ruiyang; Ye, Tiantian; Wei, Zongsu; Luo, Shuang; Yang, Zhihui; Spinney, Richard

    2015-11-17

    The sulfate radical anion (SO4•–) based oxidation of trace organic contaminants (TrOCs) has recently received great attention due to its high reactivity and low selectivity. In this study, a meta-analysis was conducted to better understand the role of functional groups on the reactivity between SO4•– and TrOCs. The results indicate that compounds in which electron transfer and addition channels dominate tend to exhibit a faster second-order rate constants (kSO4•–) than that of H–atom abstraction, corroborating the SO4•– reactivity and mechanisms observed in the individual studies. Then, a quantitative structure activity relationship (QSAR) model was developed using a sequential approach with constitutional, geometrical, electrostatic, and quantum chemical descriptors. Two descriptors, ELUMO and EHOMO energy gap (ELUMO–EHOMO) and the ratio of oxygen atoms to carbon atoms (#O:C), were found to mechanistically and statistically affect kSO4•– to a great extent with the standardized QSAR model: ln kSO4•– = 26.8–3.97 × #O:C – 0.746 × (ELUMO–EHOMO). In addition, the correlation analysis indicates that there is no dominant reaction channel for SO4•– reactions with various structurally diverse compounds. Our QSAR model provides a robust predictive tool for estimating emerging micropollutants removal using SO4•– during wastewater treatment processes.

  7. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

    PubMed

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

  8. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

    PubMed Central

    Papamokos, George; Silins, Ilona

    2016-01-01

    There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

  9. 3D-QSAR studies on 1,2,4-triazolyl 5-azaspiro [2.4]-heptanes as D3R antagonists

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Zhang, Hui

    2018-07-01

    Dopamine D3 receptor has become an attractive target in the treatment of abused drugs. 3D-QSAR studies were performed on a novel series of D3 receptor antagonists, 1,2,4-triazolyl 5-azaspiro [2.4]-heptanes, using CoMFA and CoMSIA methods. Two predictive 3D-QSAR models have been generated for the modified design of D3R antagonists. Based on the steric, electrostatic, hydrophobic and hydrogen-bond acceptor information of contour maps, key structural factors affecting the bioactivity were explored. This work gives helpful suggestions on the design of novel D3R antagonists with increased activities.

  10. A Monte Carlo-type simulation toolbox for Solar System small body dynamics: Application to the October Draconids

    NASA Astrophysics Data System (ADS)

    Kastinen, D.; Kero, J.

    2017-09-01

    We present the current status and first results from a Monte Carlo-type simulation toolbox for Solar System small body dynamics. We also present fundamental methods for evaluating the results of this type of simulations using convergence criteria. The calculations consider a body in the Solar System with a mass loss mechanism that generates smaller particles. In our application the body, or parent body, is a comet and the mass loss mechanism is a sublimation process. In order to study mass propagation from parent bodies to Earth, we use the toolbox to sample the uncertainty distributions of relevant comet parameters and to find the resulting Earth influx distributions. The initial distributions considered represent orbital elements, sublimation distance, cometary and meteoroid densities, comet and meteoroid sizes and cometary surface activity. Simulations include perturbations from all major planets, radiation pressure and the Poynting-Robertson effect. In this paper we present the results of an initial software validation performed by producing synthetic versions of the 1933, 1946, 2011 and 2012 October Draconids meteor outbursts and comparing them with observational data and previous models. The synthetic meteor showers were generated by ejecting and propagating material from the recognized parent body of the October Draconids; the comet 21P/Giacobini-Zinner. Material was ejected during 17 perihelion passages between 1866 and 1972. Each perihelion passage was sampled with 50 clones of the parent body, all producing meteoroid streams. The clones were drawn from a multidimensional Gaussian distribution on the orbital elements, with distribution variances proportional to observational uncertainties. In the simulations, each clone ejected 8000 particles. Each particle was assigned an individual weight proportional to the mass loss it represented. This generated a total of 6.7 million test particles, out of which 43 thousand entered the Earth's Hill sphere during 1900

  11. Domain-Specific QSAR Models for Identifying Potential Estrogenic Activity of Phenols (FutureTox III)

    EPA Science Inventory

    Computational tools can be used for efficient evaluation of untested chemicals for their ability to disrupt the endocrine system. We have employed previously developed global QSAR models that were trained and validated on the ToxCast/Tox21 ER assay data for virtual screening of a...

  12. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh

    2013-10-01

    Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα bindingmore » affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and

  13. Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

    NASA Astrophysics Data System (ADS)

    Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

    2013-12-01

    P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

  14. Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

    PubMed

    Liu, Huihui; Wei, Mengbi; Yang, Xianhai; Yin, Cen; He, Xiao

    2017-01-01

    Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K pew ). For chemicals with the octanol-water partition coefficient (log K ow ) <8, a TLSER model with V x (McGowan volume) and qA - (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R 2 ) and cross-validated coefficient (Q 2 ). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K OW >8, a TLSER model with V x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. A sigma factor toolbox for orthogonal gene expression in Escherichia coli

    PubMed Central

    Van Brempt, Maarten; Van Nerom, Katleen; Van Hove, Bob; Maertens, Jo; De Mey, Marjan; Charlier, Daniel

    2018-01-01

    Abstract Synthetic genetic sensors and circuits enable programmable control over timing and conditions of gene expression and, as a result, are increasingly incorporated into the control of complex and multi-gene pathways. Size and complexity of genetic circuits are growing, but stay limited by a shortage of regulatory parts that can be used without interference. Therefore, orthogonal expression and regulation systems are needed to minimize undesired crosstalk and allow for dynamic control of separate modules. This work presents a set of orthogonal expression systems for use in Escherichia coli based on heterologous sigma factors from Bacillus subtilis that recognize specific promoter sequences. Up to four of the analyzed sigma factors can be combined to function orthogonally between each other and toward the host. Additionally, the toolbox is expanded by creating promoter libraries for three sigma factors without loss of their orthogonal nature. As this set covers a wide range of transcription initiation frequencies, it enables tuning of multiple outputs of the circuit in response to different sensory signals in an orthogonal manner. This sigma factor toolbox constitutes an interesting expansion of the synthetic biology toolbox and may contribute to the assembly of more complex synthetic genetic systems in the future. PMID:29361130

  16. A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction.

    PubMed

    Abulnaga, S Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M; Onyike, Chiadi U; Ying, Sarah H; Prince, Jerry L

    2016-02-27

    The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

  17. A toolbox to visually explore cerebellar shape changes in cerebellar disease and dysfunction

    NASA Astrophysics Data System (ADS)

    Abulnaga, S. Mazdak; Yang, Zhen; Carass, Aaron; Kansal, Kalyani; Jedynak, Bruno M.; Onyike, Chiadi U.; Ying, Sarah H.; Prince, Jerry L.

    2016-03-01

    The cerebellum plays an important role in motor control and is also involved in cognitive processes. Cerebellar function is specialized by location, although the exact topographic functional relationship is not fully understood. The spinocerebellar ataxias are a group of neurodegenerative diseases that cause regional atrophy in the cerebellum, yielding distinct motor and cognitive problems. The ability to study the region-specific atrophy patterns can provide insight into the problem of relating cerebellar function to location. In an effort to study these structural change patterns, we developed a toolbox in MATLAB to provide researchers a unique way to visually explore the correlation between cerebellar lobule shape changes and function loss, with a rich set of visualization and analysis modules. In this paper, we outline the functions and highlight the utility of the toolbox. The toolbox takes as input landmark shape representations of subjects' cerebellar substructures. A principal component analysis is used for dimension reduction. Following this, a linear discriminant analysis and a regression analysis can be performed to find the discriminant direction associated with a specific disease type, or the regression line of a specific functional measure can be generated. The characteristic structural change pattern of a disease type or of a functional score is visualized by sampling points on the discriminant or regression line. The sampled points are used to reconstruct synthetic cerebellar lobule shapes. We showed a few case studies highlighting the utility of the toolbox and we compare the analysis results with the literature.

  18. User Guide for Compressible Flow Toolbox Version 2.1 for Use With MATLAB(Registered Trademark); Version 7

    NASA Technical Reports Server (NTRS)

    Melcher, Kevin J.

    2006-01-01

    This report provides a user guide for the Compressible Flow Toolbox, a collection of algorithms that solve almost 300 linear and nonlinear classical compressible flow relations. The algorithms, implemented in the popular MATLAB programming language, are useful for analysis of one-dimensional steady flow with constant entropy, friction, heat transfer, or shock discontinuities. The solutions do not include any gas dissociative effects. The toolbox also contains functions for comparing and validating the equation-solving algorithms against solutions previously published in the open literature. The classical equations solved by the Compressible Flow Toolbox are: isentropic-flow equations, Fanno flow equations (pertaining to flow of an ideal gas in a pipe with friction), Rayleigh flow equations (pertaining to frictionless flow of an ideal gas, with heat transfer, in a pipe of constant cross section.), normal-shock equations, oblique-shock equations, and Prandtl-Meyer expansion equations. At the time this report was published, the Compressible Flow Toolbox was available without cost from the NASA Software Repository.

  19. QSAR Study on the anti-tumor activity of levofloxacin-thiadiazole HDACi conjugates

    NASA Astrophysics Data System (ADS)

    Tang, Ziqiang; Feng, Hui; Chen, Yan; Yue, Wei; Feng, Changjun

    2017-12-01

    A molecular electronegativity distance vector(M t) based on 13atomic types is used to describe the structures of 19 conjugates(LHCc) of levofloxacin-thiadiazole HDAC inhibitor(HDACi) and related to the anti-tumor activity (M F and P C) of LHCc against MCF-7 and PC-3. The quantitative structure-activity relationships (QSAR) was established by using leaps-and-bounds regression analysis for the anti-tumor activities (M F and P C) of 19 above compounds to MCF-7and PC-3 along with the M t. The correlation coefficients (R 2) and the leave-one-out (LOO) cross validation R cv 2 for the M F and P C models were 0.792 and 0.679; 0.773 and 0.565, respectively. The QSAR models have favorable correlation, as well as robustness and good prediction capability by R 2, F, R cv 2, A IC F IT V IF tests. The results indicate that the molecular structural units: －CHg－(g=1, 2), －NH2, －NH－,－OH, O=, －O－, －S－ and －X are main factors which can affect the anti-tumor activity M F and PC bioactivities of these compounds directly.

  20. Basic Radar Altimetry Toolbox and Radar Altimetry Tutorial: Tools for all Altimetry Users

    NASA Astrophysics Data System (ADS)

    Rosmorduc, Vinca; Benveniste, J.; Breebaart, L.; Bronner, E.; Dinardo, S.; Earith, D.; Lucas, B. M.; Maheu, C.; Niejmeier, S.; Picot, N.

    2013-09-01

    The Basic Radar Altimetry Toolbox is an "all- altimeter" collection of tools, tutorials and documents designed to facilitate the use of radar altimetry data, including the next mission to be launched, Saral.It has been available from April 2007, and had been demonstrated during training courses and scientific meetings. Nearly 2000 people downloaded it (January 2012), with many "newcomers" to altimetry among them. Users' feedbacks, developments in altimetry, and practice, showed that new interesting features could be added. Some have been added and/or improved in version 2 to 4. Others are under development, some are in discussion for the future.The Basic Radar Altimetry Toolbox is able:- to read most distributed radar altimetry data, including the one from future missions like Saral, Jason-3- to perform some processing, data editing and statistic, - and to visualize the results.It can be used at several levels/several ways, including as an educational tool, with the graphical user interface.As part of the Toolbox, a Radar Altimetry Tutorial gives general information about altimetry, the technique involved and its applications, as well as an overview of past, present and future missions, including information on how to access data and additional software and documentation. It also presents a series of data use cases, covering all uses of altimetry over ocean, cryosphere and land, showing the basic methods for some of the most frequent manners of using altimetry data.BRAT is developed under contract with ESA and CNES. It is available at http://www.altimetry.info and http://earth.esa.int/brat/It has been available from April 2007, and had been demonstrated during training courses and scientific meetings. More than 2000 people downloaded it (as of end of September 2012), with many "newcomers" to altimetry among them, and teachers/students. Users' feedbacks, developments in altimetry, and practice, showed that new interesting features could be added. Some have been

  1. VARS-TOOL: A Comprehensive, Efficient, and Robust Sensitivity Analysis Toolbox

    NASA Astrophysics Data System (ADS)

    Razavi, S.; Sheikholeslami, R.; Haghnegahdar, A.; Esfahbod, B.

    2016-12-01

    VARS-TOOL is an advanced sensitivity and uncertainty analysis toolbox, applicable to the full range of computer simulation models, including Earth and Environmental Systems Models (EESMs). The toolbox was developed originally around VARS (Variogram Analysis of Response Surfaces), which is a general framework for Global Sensitivity Analysis (GSA) that utilizes the variogram/covariogram concept to characterize the full spectrum of sensitivity-related information, thereby providing a comprehensive set of "global" sensitivity metrics with minimal computational cost. VARS-TOOL is unique in that, with a single sample set (set of simulation model runs), it generates simultaneously three philosophically different families of global sensitivity metrics, including (1) variogram-based metrics called IVARS (Integrated Variogram Across a Range of Scales - VARS approach), (2) variance-based total-order effects (Sobol approach), and (3) derivative-based elementary effects (Morris approach). VARS-TOOL is also enabled with two novel features; the first one being a sequential sampling algorithm, called Progressive Latin Hypercube Sampling (PLHS), which allows progressively increasing the sample size for GSA while maintaining the required sample distributional properties. The second feature is a "grouping strategy" that adaptively groups the model parameters based on their sensitivity or functioning to maximize the reliability of GSA results. These features in conjunction with bootstrapping enable the user to monitor the stability, robustness, and convergence of GSA with the increase in sample size for any given case study. VARS-TOOL has been shown to achieve robust and stable results within 1-2 orders of magnitude smaller sample sizes (fewer model runs) than alternative tools. VARS-TOOL, available in MATLAB and Python, is under continuous development and new capabilities and features are forthcoming.

  2. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

    PubMed

    Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

    2018-02-12

    We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

  3. 40 CFR 141.717 - Pre-filtration treatment toolbox components.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... surface water or GWUDI source. (c) Bank filtration. Systems receive Cryptosporidium treatment credit for... paragraph. Systems using bank filtration when they begin source water monitoring under § 141.701(a) must... 40 Protection of Environment 23 2011-07-01 2011-07-01 false Pre-filtration treatment toolbox...

  4. Toolbox for Research, or how to facilitate a central data management in small-scale research projects.

    PubMed

    Bialke, Martin; Rau, Henriette; Thamm, Oliver C; Schuldt, Ronny; Penndorf, Peter; Blumentritt, Arne; Gött, Robert; Piegsa, Jens; Bahls, Thomas; Hoffmann, Wolfgang

    2018-01-25

    In most research projects budget, staff and IT infrastructures are limiting resources. Especially for small-scale registries and cohort studies professional IT support and commercial electronic data capture systems are too expensive. Consequently, these projects use simple local approaches (e.g. Excel) for data capture instead of a central data management including web-based data capture and proper research databases. This leads to manual processes to merge, analyze and, if possible, pseudonymize research data of different study sites. To support multi-site data capture, storage and analyses in small-scall research projects, corresponding requirements were analyzed within the MOSAIC project. Based on the identified requirements, the Toolbox for Research was developed as a flexible software solution for various research scenarios. Additionally, the Toolbox facilitates data integration of research data as well as metadata by performing necessary procedures automatically. Also, Toolbox modules allow the integration of device data. Moreover, separation of personally identifiable information and medical data by using only pseudonyms for storing medical data ensures the compliance to data protection regulations. This pseudonymized data can then be exported in SPSS format in order to enable scientists to prepare reports and analyses. The Toolbox for Research was successfully piloted in the German Burn Registry in 2016 facilitating the documentation of 4350 burn cases at 54 study sites. The Toolbox for Research can be downloaded free of charge from the project website and automatically installed due to the use of Docker technology.

  5. Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis.

    PubMed

    Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Duchowicz, Pablo R; Torrens, Francisco; Estrada, Mario R

    2014-01-01

    Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

  6. nSTAT: Open-Source Neural Spike Train Analysis Toolbox for Matlab

    PubMed Central

    Cajigas, I.; Malik, W.Q.; Brown, E.N.

    2012-01-01

    Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - Generalized Linear Model (PPGLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab®. By adopting an Object-Oriented Programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. PMID:22981419

  7. EEGVIS: A MATLAB Toolbox for Browsing, Exploring, and Viewing Large Datasets.

    PubMed

    Robbins, Kay A

    2012-01-01

    Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments. Researchers also do not have a convenient method of method of visually assessing the effects of applying any stage in a processing pipeline. EEGVIS is a MATLAB toolbox that allows users to quickly explore multi-channel EEG and other large array-based data sets using multi-scale drill-down techniques. Customizable summary views reveal potentially interesting sections of data, which users can explore further by clicking to examine using detailed viewing components. The viewer and a companion browser are built on our MoBBED framework, which has a library of modular viewing components that can be mixed and matched to best reveal structure. Users can easily create new viewers for their specific data without any programming during the exploration process. These viewers automatically support pan, zoom, resizing of individual components, and cursor exploration. The toolbox can be used directly in MATLAB at any stage in a processing pipeline, as a plug-in for EEGLAB, or as a standalone precompiled application without MATLAB running. EEGVIS and its supporting packages are freely available under the GNU general public license at http://visual.cs.utsa.edu/eegvis.

  8. Towards discovering dual functional inhibitors against both wild type and K103N mutant HIV-1 reverse transcriptases: molecular docking and QSAR studies on 4,1-benzoxazepinone analogues

    NASA Astrophysics Data System (ADS)

    Zhang, Zhenshan; Zheng, Mingyue; Du, Li; Shen, Jianhua; Luo, Xiaomin; Zhu, Weiliang; Jiang, Hualiang

    2006-05-01

    To find useful information for discovering dual functional inhibitors against both wild type (WT) and K103N mutant reverse transcriptases (RTs) of HIV-1, molecular docking and 3D-QSAR approaches were applied to a set of twenty-five 4,1-benzoxazepinone analogues of efavirenz (SUSTIVA®), some of them are active against the two RTs. 3D-QSAR models were constructed, based on their binding conformations determined by molecular docking, with r 2 cv values ranging from 0.656 to 0.834 for CoMFA and CoMSIA, respectively. The models were then validated to be highly predictive and extrapolative by inhibitors in two test sets with different molecular skeletons. Furthermore, CoMFA models were found to be well matched with the binding sites of both WT and K103N RTs. Finally, a reasonable pharmacophore model of 4,1-benzoxazepinones were established. The application of the model not only successfully differentiated the experimentally determined inhibitors from non-inhibitors, but also discovered two potent inhibitors from the compound database SPECS. On the basis of both the 3D-QSAR and pharmacophore models, new clues for discovering and designing potent dual functional drug leads against HIV-1 were proposed: (i) adopting positively charged aliphatic group at the cis-substituent of C3; (ii) reducing the electronic density at the position of O4; (iii) positioning a small branched aliphatic group at position of C5; (iv) using the negatively charged bulky substituents at position of C7.

  9. MatTAP: A MATLAB toolbox for the control and analysis of movement synchronisation experiments.

    PubMed

    Elliott, Mark T; Welchman, Andrew E; Wing, Alan M

    2009-02-15

    Investigating movement timing and synchronisation at the sub-second range relies on an experimental setup that has high temporal fidelity, is able to deliver output cues and can capture corresponding responses. Modern, multi-tasking operating systems make this increasingly challenging when using standard PC hardware and programming languages. This paper describes a new free suite of tools (available from http://www.snipurl.com/mattap) for use within the MATLAB programming environment, compatible with Microsoft Windows and a range of data acquisition hardware. The toolbox allows flexible generation of timing cues with high temporal accuracy, the capture and automatic storage of corresponding participant responses and an integrated analysis module for the rapid processing of results. A simple graphical user interface is used to navigate the toolbox and so can be operated easily by users not familiar with programming languages. However, it is also fully extensible and customisable, allowing adaptation for individual experiments and facilitating the addition of new modules in future releases. Here we discuss the relevance of the MatTAP (MATLAB Timing Analysis Package) toolbox to current timing experiments and compare its use to alternative methods. We validate the accuracy of the analysis module through comparison to manual observation methods and replicate a previous sensorimotor synchronisation experiment to demonstrate the versatility of the toolbox features demanded by such movement synchronisation paradigms.

  10. ElectroMagnetoEncephalography Software: Overview and Integration with Other EEG/MEG Toolboxes

    PubMed Central

    Peyk, Peter; De Cesarei, Andrea; Junghöfer, Markus

    2011-01-01

    EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section. PMID:21577273

  11. ElectroMagnetoEncephalography software: overview and integration with other EEG/MEG toolboxes.

    PubMed

    Peyk, Peter; De Cesarei, Andrea; Junghöfer, Markus

    2011-01-01

    EMEGS (electromagnetic encephalography software) is a MATLAB toolbox designed to provide novice as well as expert users in the field of neuroscience with a variety of functions to perform analysis of EEG and MEG data. The software consists of a set of graphical interfaces devoted to preprocessing, analysis, and visualization of electromagnetic data. Moreover, it can be extended using a plug-in interface. Here, an overview of the capabilities of the toolbox is provided, together with a simple tutorial for both a standard ERP analysis and a time-frequency analysis. Latest features and future directions of the software development are presented in the final section.

  12. Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs

    NASA Astrophysics Data System (ADS)

    Adhikari, Nilanjan; Amin, Sk. Abdul; Saha, Achintya; Jha, Tarun

    2018-03-01

    Matrix metalloproteinase-2 (MMP-2) is a promising pharmacological target for designing potential anticancer drugs. MMP-2 plays critical functions in apoptosis by cleaving the DNA repair enzyme namely poly (ADP-ribose) polymerase (PARP). Moreover, MMP-2 expression triggers the vascular endothelial growth factor (VEGF) having a positive influence on tumor size, invasion, and angiogenesis. Therefore, it is an urgent need to develop potential MMP-2 inhibitors without any toxicity but better pharmacokinetic property. In this article, robust validated multi-quantitative structure-activity relationship (QSAR) modeling approaches were attempted on a dataset of 222 MMP-2 inhibitors to explore the important structural and pharmacophoric requirements for higher MMP-2 inhibition. Different validated regression and classification-based QSARs, pharmacophore mapping and 3D-QSAR techniques were performed. These results were challenged and subjected to further validation to explain 24 in house MMP-2 inhibitors to judge the reliability of these models further. All these models were individually validated internally as well as externally and were supported and validated by each other. These results were further justified by molecular docking analysis. Modeling techniques adopted here not only helps to explore the necessary structural and pharmacophoric requirements but also for the overall validation and refinement techniques for designing potential MMP-2 inhibitors.

  13. Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

    NASA Technical Reports Server (NTRS)

    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Jewell, A. D.; Zhou, H.; Manatt, K.; Kisor, A. K.

    2005-01-01

    We report a Quantitative Structure-Activity Relationships (QSAR) study using Genetic Function Approximations (GFA) to describe the polymer-carbon composite sensor activities in the JPL Electronic Nose, when exposed to chemical vapors at parts-per-million concentration levels.

  14. ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

    NASA Astrophysics Data System (ADS)

    Lecocq, Thomas; Megies, Tobias; Krischer, Lion; Sales de Andrade, Elliott; Barsch, Robert; Beyreuther, Moritz

    2016-04-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides * read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, * a comprehensive signal processing toolbox tuned to the needs of seismologists, * integrated access to all large data centers, web services and databases, and * convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software. ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it. This contribution will give a short introduction and overview of ObsPy and highlight a number of use cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

  15. ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

    NASA Astrophysics Data System (ADS)

    Krischer, L.; Megies, T.; Sales de Andrade, E.; Barsch, R.; Beyreuther, M.

    2015-12-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, a comprehensive signal processing toolbox tuned to the needs of seismologists, integrated access to all large data centers, web services and databases, and convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software.ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it.This contribution will give a short introduction and overview of ObsPy and highlight a number of us cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

  16. Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

    PubMed

    Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

    2015-01-01

    The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with

  17. MTpy: A Python toolbox for magnetotellurics

    NASA Astrophysics Data System (ADS)

    Krieger, Lars; Peacock, Jared R.

    2014-11-01

    We present the software package MTpy that allows handling, processing, and imaging of magnetotelluric (MT) data sets. Written in Python, the code is open source, containing sub-packages and modules for various tasks within the standard MT data processing and handling scheme. Besides the independent definition of classes and functions, MTpy provides wrappers and convenience scripts to call standard external data processing and modelling software. In its current state, modules and functions of MTpy work on raw and pre-processed MT data. However, opposite to providing a static compilation of software, we prefer to introduce MTpy as a flexible software toolbox, whose contents can be combined and utilised according to the respective needs of the user. Just as the overall functionality of a mechanical toolbox can be extended by adding new tools, MTpy is a flexible framework, which will be dynamically extended in the future. Furthermore, it can help to unify and extend existing codes and algorithms within the (academic) MT community. In this paper, we introduce the structure and concept of MTpy. Additionally, we show some examples from an everyday work-flow of MT data processing: the generation of standard EDI data files from raw electric (E-) and magnetic flux density (B-) field time series as input, the conversion into MiniSEED data format, as well as the generation of a graphical data representation in the form of a Phase Tensor pseudosection.

  18. An integrated QSAR-PBK/D modelling approach for predicting detoxification and DNA adduct formation of 18 acyclic food-borne α,β-unsaturated aldehydes

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kiwamoto, R., E-mail: reiko.kiwamoto@wur.nl; Spenkelink, A.; Rietjens, I.M.C.M.

    Acyclic α,β-unsaturated aldehydes present in food raise a concern because the α,β-unsaturated aldehyde moiety is considered a structural alert for genotoxicity. However, controversy remains on whether in vivo at realistic dietary exposure DNA adduct formation is significant. The aim of the present study was to develop physiologically based kinetic/dynamic (PBK/D) models to examine dose-dependent detoxification and DNA adduct formation of a group of 18 food-borne acyclic α,β-unsaturated aldehydes without 2- or 3-alkylation, and with no more than one conjugated double bond. Parameters for the PBK/D models were obtained using quantitative structure–activity relationships (QSARs) defined with a training set of sixmore » selected aldehydes. Using the QSARs, PBK/D models for the other 12 aldehydes were defined. Results revealed that DNA adduct formation in the liver increases with decreasing bulkiness of the molecule especially due to less efficient detoxification. 2-Propenal (acrolein) was identified to induce the highest DNA adduct levels. At realistic dietary intake, the predicted DNA adduct levels for all aldehydes were two orders of magnitude lower than endogenous background levels observed in disease free human liver, suggesting that for all 18 aldehydes DNA adduct formation is negligible at the relevant levels of dietary intake. The present study provides a proof of principle for the use of QSAR-based PBK/D modelling to facilitate group evaluations and read-across in risk assessment. - Highlights: • Physiologically based in silico models were made for 18 α,β-unsaturated aldehydes. • Kinetic parameters were determined by in vitro incubations and a QSAR approach. • DNA adduct formation was negligible at levels relevant for dietary intake. • The use of QSAR-based PBK/D modelling facilitates group evaluations and read-across.« less

  19. Review of qualitative approaches for the construction industry: designing a risk management toolbox.

    PubMed

    Zalk, David M; Spee, Ton; Gillen, Matt; Lentz, Thomas J; Garrod, Andrew; Evans, Paul; Swuste, Paul

    2011-06-01

    This paper presents the framework and protocol design for a construction industry risk management toolbox. The construction industry needs a comprehensive, systematic approach to assess and control occupational risks. These risks span several professional health and safety disciplines, emphasized by multiple international occupational research agenda projects including: falls, electrocution, noise, silica, welding fumes, and musculoskeletal disorders. Yet, the International Social Security Association says, "whereas progress has been made in safety and health, the construction industry is still a high risk sector." Small- and medium-sized enterprises (SMEs) employ about 80% of the world's construction workers. In recent years a strategy for qualitative occupational risk management, known as Control Banding (CB) has gained international attention as a simplified approach for reducing work-related risks. CB groups hazards into stratified risk 'bands', identifying commensurate controls to reduce the level of risk and promote worker health and safety. We review these qualitative solutions-based approaches and identify strengths and weaknesses toward designing a simplified CB 'toolbox' approach for use by SMEs in construction trades. This toolbox design proposal includes international input on multidisciplinary approaches for performing a qualitative risk assessment determining a risk 'band' for a given project. Risk bands are used to identify the appropriate level of training to oversee construction work, leading to commensurate and appropriate control methods to perform the work safely. The Construction Toolbox presents a review-generated format to harness multiple solutions-based national programs and publications for controlling construction-related risks with simplified approaches across the occupational safety, health and hygiene professions.

  20. Review of Qualitative Approaches for the Construction Industry: Designing a Risk Management Toolbox

    PubMed Central

    Spee, Ton; Gillen, Matt; Lentz, Thomas J.; Garrod, Andrew; Evans, Paul; Swuste, Paul

    2011-01-01

    Objectives This paper presents the framework and protocol design for a construction industry risk management toolbox. The construction industry needs a comprehensive, systematic approach to assess and control occupational risks. These risks span several professional health and safety disciplines, emphasized by multiple international occupational research agenda projects including: falls, electrocution, noise, silica, welding fumes, and musculoskeletal disorders. Yet, the International Social Security Association says, "whereas progress has been made in safety and health, the construction industry is still a high risk sector." Methods Small- and medium-sized enterprises (SMEs) employ about 80% of the world's construction workers. In recent years a strategy for qualitative occupational risk management, known as Control Banding (CB) has gained international attention as a simplified approach for reducing work-related risks. CB groups hazards into stratified risk 'bands', identifying commensurate controls to reduce the level of risk and promote worker health and safety. We review these qualitative solutions-based approaches and identify strengths and weaknesses toward designing a simplified CB 'toolbox' approach for use by SMEs in construction trades. Results This toolbox design proposal includes international input on multidisciplinary approaches for performing a qualitative risk assessment determining a risk 'band' for a given project. Risk bands are used to identify the appropriate level of training to oversee construction work, leading to commensurate and appropriate control methods to perform the work safely. Conclusion The Construction Toolbox presents a review-generated format to harness multiple solutions-based national programs and publications for controlling construction-related risks with simplified approaches across the occupational safety, health and hygiene professions. PMID:22953194

  1. 3D-QSAR and virtual screening studies of thiazolidine-2,4-dione analogs: Validation of experimental inhibitory potencies towards PIM-1 kinase

    NASA Astrophysics Data System (ADS)

    Asati, Vivek; Bharti, Sanjay Kumar; Budhwani, Ashok Kumar

    2017-04-01

    The proviral insertion site in moloney murine leukemia virus (PIM) is a family of serine/threonine kinase of Ca2+-calmodulin-dependent protein kinase (CAMK) group which is responsible for the activation and regulation of cellular transcription and translation. The three isoforms of PIM kinase (PIM-1, PIM-2 and PIM-3) share high homology and functional idleness are widely expressed and involved in a variety of biological processes including cell survival, proliferation, differentiation and apoptosis. Altered expression of PIM-1 kinase correlated with hematologic malignancies and solid tumors. In the present study, atom-based 3D-QSAR, docking and virtual screening studies have been performed on a series of thiazolidine-2,4-dione derivatives as PIM-1 kinase inhibitors. 3D-QSAR and docking approach has shortlisted the most active thiazolidine-2,4-dione derivatives such as 28, 31, 33 and 35 with the incorporation of more than one structural feature in a single molecule. External validations by various parameters and molecular docking studies at the active site of PIM-1 kinase have proved the reliability of the developed 3D-QSAR model. The generated pharmacophore (AADHR.33) from 3D-QSAR study was used for screening of drug like compounds from ZINC database, where ZINC15056464 and ZINC83292944 showed potential binding affinities at the active site amino acid residues (LYS67, GLU171, ASP128 and ASP186) of PIM-1 kinase.

  2. Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

    PubMed

    Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

    2018-01-01

    The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Synthesis, biological evaluation, QSAR study and molecular docking of novel N-(4-amino carbonylpiperazinyl) (thio)phosphoramide derivatives as cholinesterase inhibitors.

    PubMed

    Gholivand, Khodayar; Ebrahimi Valmoozi, Ali Asghar; Bonsaii, Mahyar

    2014-06-01

    Novel (thio)phosphoramidate derivatives based on piperidincarboxamide with the general formula of (NH2-C(O)-C5H9N)-P(X=O,S)R1R2 (1-5) and (NH2-C(O)-C5H9N)2-P(O)R (6-9) were synthesized and characterized by (31)P, (13)C, (1)H NMR, IR spectroscopy. Furthermore, the crystal structure of compound (NH2-C(O)-C5H9N)2-P(O)(OC6H5) (6) was investigated. The activities of derivatives on cholinesterases (ChE) were determined using a modified Ellman's method. Also the mixed-type mechanisms of these compounds were evaluated by Lineweaver-Burk plots. Molecular docking and quantitative structure-activity relationship (QSAR) were used to understand the relationship between molecular structural features and anti-ChE activity, and to predict the binding affinity of phosphoramido-piperidinecarboxamides (PAPCAs) to ChE receptors. From molecular docking analysis, noncovalent interactions especially hydrogen bonding as well as hydrophobic was found between PAPCAs and ChE. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. DFT-QSAR models for ChE enzymes demonstrated the importance of electrophilicity parameter in describing the anti-AChE and anti-BChE activities of the synthesized compounds. The correlation matrix of QSAR models and docking analysis confirmed that electrophilicity descriptor can control the influence of the hydrophobic properties of P=(O, S) and CO functional groups of PAPCA derivatives in the inhibition of human ChE enzymes. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Elucidation of chemosensitization effect of acridones in cancer cell lines: Combined pharmacophore modeling, 3D QSAR, and molecular dynamics studies.

    PubMed

    Gade, Deepak Reddy; Makkapati, Amareswararao; Yarlagadda, Rajesh Babu; Peters, Godefridus J; Sastry, B S; Rajendra Prasad, V V S

    2018-06-01

    Overexpression of P-glycoprotein (P-gp) leads to the emergence of multidrug resistance (MDR) in cancer treatment. Acridones have the potential to reverse MDR and sensitize cells. In the present study, we aimed to elucidate the chemosensitization potential of acridones by employing various molecular modelling techniques. Pharmacophore modeling was performed for the dataset of chemosensitizing acridones earlier proved for cytotoxic activity against MCF7 breast cancer cell line. Gaussian-based QSAR studies also performed to predict the favored and disfavored region of the acridone molecules. Molecular dynamics simulations were performed for compound 10 and human P-glycoprotein (obtained from Homology modeling). An efficient pharmacophore containing 2 hydrogen bond acceptors and 3 aromatic rings (AARRR.14) was identified. NCI 2012 chemical database was screened against AARRR.14 CPH and identified 25 best-fit molecules. Potential regions of the compound were identified through Field (Gaussian) based QSAR. Regression analysis of atom-based QSAR resulted in r 2 of 0.95 and q 2 of 0.72, whereas, regression analysis of field-based QSAR resulted in r 2 of 0.92 and q 2 of 0.87 along with r 2 cv as 0.71. The fate of the acridone molecule (compound 10) in the P-glycoprotein environment is analyzed through analyzing the conformational changes occurring during the molecular dynamics simulations. Combined data of different in silico techniques provided basis for deeper understanding of structural and mechanistic insights of interaction phenomenon of acridones with P-glycoprotein and also as strategic basis for designing more potent molecules for anti-cancer and multidrug resistance reversal activities. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. An Early Years Toolbox for Assessing Early Executive Function, Language, Self-Regulation, and Social Development: Validity, Reliability, and Preliminary Norms

    PubMed Central

    Howard, Steven J.; Melhuish, Edward

    2016-01-01

    Several methods of assessing executive function (EF), self-regulation, language development, and social development in young children have been developed over previous decades. Yet new technologies make available methods of assessment not previously considered. In resolving conceptual and pragmatic limitations of existing tools, the Early Years Toolbox (EYT) offers substantial advantages for early assessment of language, EF, self-regulation, and social development. In the current study, results of our large-scale administration of this toolbox to 1,764 preschool and early primary school students indicated very good reliability, convergent validity with existing measures, and developmental sensitivity. Results were also suggestive of better capture of children’s emerging abilities relative to comparison measures. Preliminary norms are presented, showing a clear developmental trajectory across half-year age groups. The accessibility of the EYT, as well as its advantages over existing measures, offers considerably enhanced opportunities for objective measurement of young children’s abilities to enable research and educational applications. PMID:28503022

  6. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

    PubMed

    Dobchev, Dimitar; Karelson, Mati

    2016-07-01

    Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

  7. QSAR Study of p56lck Protein Tyrosine Kinase Inhibitory Activity of Flavonoid Derivatives Using MLR and GA-PLS

    PubMed Central

    Fassihi, Afshin; Sabet, Razieh

    2008-01-01

    Quantitative relationships between molecular structure and p56lck protein tyrosine kinase inhibitory activity of 50 flavonoid derivatives are discovered by MLR and GA-PLS methods. Different QSAR models revealed that substituent electronic descriptors (SED) parameters have significant impact on protein tyrosine kinase inhibitory activity of the compounds. Between the two statistical methods employed, GA-PLS gave superior results. The resultant GA-PLS model had a high statistical quality (R2 = 0.74 and Q2 = 0.61) for predicting the activity of the inhibitors. The models proposed in the present work are more useful in describing QSAR of flavonoid derivatives as p56lck protein tyrosine kinase inhibitors than those provided previously. PMID:19325836

  8. Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

    PubMed Central

    Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

    2006-01-01

    In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

  9. ERPLAB: an open-source toolbox for the analysis of event-related potentials

    PubMed Central

    Lopez-Calderon, Javier; Luck, Steven J.

    2014-01-01

    ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations. PMID:24782741

  10. ERPLAB: an open-source toolbox for the analysis of event-related potentials.

    PubMed

    Lopez-Calderon, Javier; Luck, Steven J

    2014-01-01

    ERPLAB toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP) data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB's EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB's tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user's guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.

  11. QSAR and docking based semi-synthesis and in vitro evaluation of 18 β-glycyrrhetinic acid derivatives against human lung cancer cell line A-549.

    PubMed

    Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar

    2013-12-01

    For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

  12. Versatile Cas9-Driven Subpopulation Selection Toolbox for Lactococcus lactis.

    PubMed

    van der Els, Simon; James, Jennelle K; Kleerebezem, Michiel; Bron, Peter A

    2018-04-15

    CRISPR-Cas9 technology has been exploited for the removal or replacement of genetic elements in a wide range of prokaryotes and eukaryotes. Here, we describe the extension of the Cas9 application toolbox to the industrially important dairy species Lactococcus lactis The Cas9 expression vector pLABTarget, encoding the Streptocccus pyogenes Cas9 under the control of a constitutive promoter, was constructed, allowing plug and play introduction of short guide RNA (sgRNA) sequences to target specific genetic loci. Introduction of a pepN -targeting derivative of pLABTarget into L. lactis strain MG1363 led to a strong reduction in the number of transformants obtained, which did not occur in a pepN deletion derivative of the same strain, demonstrating the specificity and lethality of the Cas9-mediated double-strand breaks in the lactococcal chromosome. Moreover, the same pLABTarget derivative allowed the selection of a pepN deletion subpopulation from its corresponding single-crossover plasmid integrant precursor, accelerating the construction and selection of gene-specific deletion derivatives in L. lactis Finally, pLABTarget, which contained sgRNAs designed to target mobile genetic elements, allowed the effective curing of plasmids, prophages, and integrative conjugative elements (ICEs). These results establish that pLABTarget enables the effective exploitation of Cas9 targeting in L. lactis , while the broad-host-range vector used suggests that this toolbox could readily be expanded to other Gram-positive bacteria. IMPORTANCE Mobile genetic elements in Lactococcus lactis and other lactic acid bacteria (LAB) play an important role in dairy fermentation, having both positive and detrimental effects during the production of fermented dairy products. The pLABTarget vector offers an efficient cloning platform for Cas9 application in lactic acid bacteria. Targeting Cas9 toward mobile genetic elements enabled their effective curing, which is of particular interest in the

  13. Report on the Current Inventory of the Toolbox for Plant Cell Wall Analysis: Proteinaceous and Small Molecular Probes

    PubMed Central

    Rydahl, Maja G.; Hansen, Aleksander R.; Kračun, Stjepan K.; Mravec, Jozef

    2018-01-01

    Plant cell walls are highly complex structures composed of diverse classes of polysaccharides, proteoglycans, and polyphenolics, which have numerous roles throughout the life of a plant. Significant research efforts aim to understand the biology of this cellular organelle and to facilitate cell-wall-based industrial applications. To accomplish this, researchers need to be provided with a variety of sensitive and specific detection methods for separate cell wall components, and their various molecular characteristics in vitro as well as in situ. Cell wall component-directed molecular detection probes (in short: cell wall probes, CWPs) are an essential asset to the plant glycobiology toolbox. To date, a relatively large set of CWPs has been produced—mainly consisting of monoclonal antibodies, carbohydrate-binding modules, synthetic antibodies produced by phage display, and small molecular probes. In this review, we summarize the state-of-the-art knowledge about these CWPs; their classification and their advantages and disadvantages in different applications. In particular, we elaborate on the recent advances in non-conventional approaches to the generation of novel CWPs, and identify the remaining gaps in terms of target recognition. This report also highlights the addition of new “compartments” to the probing toolbox, which is filled with novel chemical biology tools, such as metabolic labeling reagents and oligosaccharide conjugates. In the end, we also forecast future developments in this dynamic field. PMID:29774041

  14. The Radiology Resident iPad Toolbox: an educational and clinical tool for radiology residents.

    PubMed

    Sharpe, Emerson E; Kendrick, Michael; Strickland, Colin; Dodd, Gerald D

    2013-07-01

    Tablet computing and mobile resources are the hot topics in technology today, with that interest spilling into the medical field. To improve resident education, a fully configured iPad, referred to as the "Radiology Resident iPad Toolbox," was created and implemented at the University of Colorado. The goal was to create a portable device with comprehensive educational, clinical, and communication tools that would contain all necessary resources for an entire 4-year radiology residency. The device was distributed to a total of 34 radiology residents (8 first-year residents, 8 second-year residents, 9 third-year residents, and 9 fourth-year residents). This article describes the process used to develop and deploy the device, provides a distillation of useful applications and resources decided upon after extensive evaluation, and assesses the impact this device had on resident education. The Radiology Resident iPad Toolbox is a cost-effective, portable, educational instrument that has increased studying efficiency; improved access to study materials such as books, radiology cases, lectures, and web-based resources; and increased interactivity in educational conferences and lectures through the use of audience-response software, with questions geared toward the new ABR board format. This preconfigured tablet fully embraces the technology shift into mobile computing and represents a paradigm shift in educational strategy. Copyright © 2013 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  15. Expanding the UniFrac Toolbox

    PubMed Central

    2016-01-01

    The UniFrac distance metric is often used to separate groups in microbiome analysis, but requires a constant sequencing depth to work properly. Here we demonstrate that unweighted UniFrac is highly sensitive to rarefaction instance and to sequencing depth in uniform data sets with no clear structure or separation between groups. We show that this arises because of subcompositional effects. We introduce information UniFrac and ratio UniFrac, two new weightings that are not as sensitive to rarefaction and allow greater separation of outliers than classic unweighted and weighted UniFrac. With this expansion of the UniFrac toolbox, we hope to empower researchers to extract more varied information from their data. PMID:27632205

  16. An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes

    PubMed Central

    Shafqat-Abbasi, Hamdah; Kowalewski, Jacob M; Kiss, Alexa; Gong, Xiaowei; Hernandez-Varas, Pablo; Berge, Ulrich; Jafari-Mamaghani, Mehrdad; Lock, John G; Strömblad, Staffan

    2016-01-01

    Mesenchymal (lamellipodial) migration is heterogeneous, although whether this reflects progressive variability or discrete, 'switchable' migration modalities, remains unclear. We present an analytical toolbox, based on quantitative single-cell imaging data, to interrogate this heterogeneity. Integrating supervised behavioral classification with multivariate analyses of cell motion, membrane dynamics, cell-matrix adhesion status and F-actin organization, this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes, termed 'Continuous' and 'Discontinuous'. Quantitative mode comparisons reveal differences in cell motion, spatiotemporal coordination of membrane protrusion/retraction, and how cells within each mode reorganize with changed cell speed. These modes thus represent distinctive migratory strategies. Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting (fibronectin, talin, ROCK), including 'adaptive switching' between Continuous (favored at high adhesion/full contraction) and Discontinuous (low adhesion/inhibited contraction) modes. Overall, this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration. DOI: http://dx.doi.org/10.7554/eLife.11384.001 PMID:26821527

  17. Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Li, Xiaolin; Ye, Li; Wang, Xiaoxiang

    2012-12-15

    Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 andmore » non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.« less

  18. Open Babel: An open chemical toolbox

    PubMed Central

    2011-01-01

    Background A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. Results We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Conclusions Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org. PMID:21982300

  19. Novel dimer based descriptors with solvational computation for QSAR study of oxadiazoylbenzoyl-ureas as novel insect-growth regulators.

    PubMed

    Fan, Feng; Cheng, Jiagao; Li, Zhong; Xu, Xiaoyong; Qian, Xuhong

    2010-02-01

    Molecular aggregation state of bioactive compounds plays a key role in its bio-interactive procedure. In this article, based on the structure information of dimers, the simplest model of molecular aggregation state, and combined with solvational computation, total four descriptors (DeltaV, MR2, DeltaE(1), and DeltaE(2)) were calculated for QSAR study of a novel insect-growth regulator, N-(5-phenyl-1,3,4-oxadiazol-2-yl)-N'-benzoyl urea. Two QSAR models were constructed with r(2) = 0.671, q(2) = 0.516 and r(2) = 0.816, q(2) = 0.695, respectively. It implicates that the bioactivity may strongly depend on the characters of molecular aggregation state, especially on the dimeric transport ability from oil phase to water phase. Copyright 2009 Wiley Periodicals, Inc.

  20. The Basic Radar Altimetry Toolbox for Sentinel 3 Users

    NASA Astrophysics Data System (ADS)

    Lucas, Bruno; Rosmorduc, Vinca; Niemeijer, Sander; Bronner, Emilie; Dinardo, Salvatore; Benveniste, Jérôme

    2013-04-01

    altimetry, showing its applications in different fields such as Oceanography, Cryosphere, Geodesy, Hydrology among others. Included are also "data use cases", with step-by-step examples, on how to use the toolbox in the different contexts. The upcoming release that is on the forge will focus on Sentinel 3 Surface Topography Mission that is build on the successful heritage of ERS, Envisat and Cryosat. The first of the two sentinel is expected to be launched in 2014. It will have on-board a dual-frequency (Ku and C band) advanced Synthetic Aperture Radar Altimeter and will provide measurements at a resolution of ~300m in SAR mode along track. Sentinel 3 will provide exact measurements of sea-surface height along with accurate topography measurements over sea ice, ice sheets, rivers and lakes. The future version will provide, among other enhancements, support for reading the upcoming S3 datasets and specific "use-cases" for SAR altimetry in order to train the users and made them aware of the great potential of SAR altimetery for coastal and inland applications. The BRAT software is distributed under the GNU GPL open-source license and can be obtained, along with all the documentation (including the tutorial), on the webstite: http://earth.esa.int/brat

  1. Pharmacophore Based 3D-QSAR, Virtual Screening and Docking Studies on Novel Series of HDAC Inhibitors with Thiophen Linker as Anticancer Agents.

    PubMed

    Patel, Preeti; Singh, Avineesh; Patel, Vijay K; Jain, Deepak K; Veerasamy, Ravichandran; Rajak, Harish

    2016-01-01

    Histone deacetylase (HDAC) inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. To identify the important pharmacophoric features and correlate 3Dchemical structure with biological activity using 3D-QSAR and Pharmacophore modeling studies. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with wellassigned HDAC inhibitory activity were used for 3D-QSAR model development. Best 3D-QSAR model, which is a five partial least square (PLS) factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811), cross-validated coefficient rcv 2=0.9807 and R2 pred=0.7147 with low standard deviation (0.0952). Additionally, the selected pharmacophore model DDRRR.419 was used as a 3D query for virtual screening against the ZINC database. In the virtual screening workflow, docking studies (HTVS, SP and XP) were carried out by selecting multiple receptors (PDB ID: 1T69, 1T64, 4LXZ, 4LY1, 3MAX, 2VQQ, 3C10, 1W22). Finally, six compounds were obtained based on high scoring function (dock score -11.2278-10.2222 kcal/mol) and diverse structures. The structure activity correlation was established using virtual screening, docking, energetic based pharmacophore modelling, pharmacophore, atom based 3D QSAR models and their validation. The outcomes of these studies could be further employed for the design of novel HDAC inhibitors for anticancer activity.

  2. Toolboxes for cyanobacteria: Recent advances and future direction.

    PubMed

    Sun, Tao; Li, Shubin; Song, Xinyu; Diao, Jinjin; Chen, Lei; Zhang, Weiwen

    2018-05-03

    Photosynthetic cyanobacteria are important primary producers and model organisms for studying photosynthesis and elements cycling on earth. Due to the ability to absorb sunlight and utilize carbon dioxide, cyanobacteria have also been proposed as renewable chassis for carbon-neutral "microbial cell factories". Recent progresses on cyanobacterial synthetic biology have led to the successful production of more than two dozen of fuels and fine chemicals directly from CO 2 , demonstrating their potential for scale-up application in the future. However, compared with popular heterotrophic chassis like Escherichia coli and Saccharomyces cerevisiae, where abundant genetic tools are available for manipulations at levels from single gene, pathway to whole genome, limited genetic tools are accessible to cyanobacteria. Consequently, this significant technical hurdle restricts both the basic biological researches and further development and application of these renewable systems. Though still lagging the heterotrophic chassis, the vital roles of genetic tools in tuning of gene expression, carbon flux re-direction as well as genome-wide manipulations have been increasingly recognized in cyanobacteria. In recent years, significant progresses on developing and introducing new and efficient genetic tools have been made for cyanobacteria, including promoters, riboswitches, ribosome binding site engineering, clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease (CRISPR/Cas) systems, small RNA regulatory tools and genome-scale modeling strategies. In this review, we critically summarize recent advances on development and applications as well as technical limitations and future directions of the genetic tools in cyanobacteria. In addition, toolboxes feasible for using in large-scale cultivation are also briefly discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

  3. 40 CFR 141.715 - Microbial toolbox options for meeting Cryptosporidium treatment requirements.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... criteria are in § 141.716(b). Pre Filtration Toolbox Options (3) Presedimentation basin with coagulation 0... separate granular media filtration stage if treatment train includes coagulation prior to first filter...

  4. 40 CFR 141.715 - Microbial toolbox options for meeting Cryptosporidium treatment requirements.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... criteria are in § 141.716(b). Pre Filtration Toolbox Options (3) Presedimentation basin with coagulation 0... separate granular media filtration stage if treatment train includes coagulation prior to first filter...

  5. Focused Field Investigations for Sewer Condition Assessment with EPA SSOAP Toolbox

    EPA Science Inventory

    The Nation’s sanitary sewer infrastructure is aging, and is currently one of the top national water program priorities. The U.S. Environmental Protection Agency (EPA) developed the Sanitary Sewer Overflow Analysis and Planning (SSOAP) Toolbox to assist communities in developing ...

  6. Language Measures of the NIH Toolbox Cognition Battery

    PubMed Central

    Gershon, Richard C.; Cook, Karon F.; Mungas, Dan; Manly, Jennifer J.; Slotkin, Jerry; Beaumont, Jennifer L.; Weintraub, Sandra

    2015-01-01

    Language facilitates communication and efficient encoding of thought and experience. Because of its essential role in early childhood development, in educational achievement and in subsequent life adaptation, language was included as one of the subdomains in the NIH Toolbox for the Assessment of Neurological and Behavioral Function Cognition Battery (NIHTB-CB). There are many different components of language functioning, including syntactic processing (i.e., morphology and grammar) and lexical semantics. For purposes of the NIHTB-CB, two tests of language—a picture vocabulary test and a reading recognition test—were selected by consensus based on literature reviews, iterative expert input, and a desire to assess in English and Spanish. NIHTB-CB’s picture vocabulary and reading recognition tests are administered using computer adaptive testing and scored using item response theory. Data are presented from the validation of the English versions in a sample of adults ages 20–85 years (Spanish results will be presented in a future publication). Both tests demonstrated high test–retest reliability and good construct validity compared to corresponding gold-standard measures. Scores on the NIH Toolbox measures were consistent with age-related expectations, namely, growth in language during early development, with relative stabilization into late adulthood. PMID:24960128

  7. Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

    PubMed Central

    Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

    2014-01-01

    Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the

  8. Influence of structure properties on protein-protein interactions-QSAR modeling of changes in diffusion coefficients.

    PubMed

    Bauer, Katharina Christin; Hämmerling, Frank; Kittelmann, Jörg; Dürr, Cathrin; Görlich, Fabian; Hubbuch, Jürgen

    2017-04-01

    Information about protein-protein interactions provides valuable knowledge about the phase behavior of protein solutions during the biopharmaceutical production process. Up to date it is possible to capture their overall impact by an experimentally determined potential of mean force. For the description of this potential, the second virial coefficient B22, the diffusion interaction parameter kD, the storage modulus G', or the diffusion coefficient D is applied. In silico methods do not only have the potential to predict these parameters, but also to provide deeper understanding of the molecular origin of the protein-protein interactions by correlating the data to the protein's three-dimensional structure. This methodology furthermore allows a lower sample consumption and less experimental effort. Of all in silico methods, QSAR modeling, which correlates the properties of the molecule's structure with the experimental behavior, seems to be particularly suitable for this purpose. To verify this, the study reported here dealt with the determination of a QSAR model for the diffusion coefficient of proteins. This model consisted of diffusion coefficients for six different model proteins at various pH values and NaCl concentrations. The generated QSAR model showed a good correlation between experimental and predicted data with a coefficient of determination R2 = 0.9 and a good predictability for an external test set with R2 = 0.91. The information about the properties affecting protein-protein interactions present in solution was in agreement with experiment and theory. Furthermore, the model was able to give a more detailed picture of the protein properties influencing the diffusion coefficient and the acting protein-protein interactions. Biotechnol. Bioeng. 2017;114: 821-831. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  9. INFLUENCE OF MATRIX FORMULATION ON DERMAL PERCUTANEOUS ABSORPTION OF TRIAZOLE FUNGICIDES USING QSAR AND PBPK / PD MODELS

    EPA Science Inventory

    The objective of this work is to use the Exposure Related Dose Estimating Model (ERDEM) and quantitative structure-activity relationship (QSAR) models to develop an assessment tool for human exposure assessment to triazole fungicides. A dermal exposure route is used for the physi...

  10. Designing of phenol-based β-carbonic anhydrase1 inhibitors through QSAR, molecular docking, and MD simulation approach.

    PubMed

    Ahamad, Shahzaib; Hassan, Md Imtaiyaz; Dwivedi, Neeraja

    2018-05-01

    Tuberculosis (Tb) is an airborne infectious disease caused by Mycobacterium tuberculosis. Beta-carbonic anhydrase 1 ( β-CA1 ) has emerged as one of the potential targets for new antitubercular drug development. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulation approaches were performed on a series of natural and synthetic phenol-based β-CA1 inhibitors. The developed 3D-QSAR model ( r 2  = 0.94, q 2  = 0.86, and pred_r 2  = 0.74) indicated that the steric and electrostatic factors are important parameters to modulate the bioactivity of phenolic compounds. Based on this indication, we designed 72 new phenolic inhibitors, out of which two compounds (D25 and D50) effectively stabilized β-CA1 receptor and, thus, are potential candidates for new generation antitubercular drug discovery program.

  11. Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model.

    PubMed

    Kim, J; Lee, C; Chong, Y

    2009-01-01

    Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q(2) = 0.763, r(2) = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.

  12. The Visible Signature Modelling and Evaluation ToolBox

    DTIC Science & Technology

    2008-12-01

    Technology Organisation DSTO–TR–2212 ABSTRACT A new software suite, the Visible Signature ToolBox ( VST ), has been developed to model and evaluate the...visible signatures of maritime platforms. The VST is a collection of commercial, off-the-shelf software and DSTO developed pro- grams and procedures. The...suite. The VST can be utilised to model and assess visible signatures of maritime platforms. A number of examples are presented to demonstrate the

  13. Experimental and QSAR study on the surface activities of alkyl imidazoline surfactants

    NASA Astrophysics Data System (ADS)

    Kong, Xiangjun; Qian, Chengduo; Fan, Weiyu; Liang, Zupei

    2018-03-01

    15 alkyl imidazoline surfactants with different structures were synthesized and their critical micelle concentration (CMC) and surface tension under the CMC (σcmc) in aqueous solution were measured at 298 K. 54 kinds of molecular structure descriptors were selected as independent variables and the quantitative structure-activity relationship (QSAR) between surface activities of alkyl imidazoline and molecular structure were built through the genetic function approximation (GFA) method. Experimental results showed that the maximum surface excess of alkyl imidazoline molecules at the gas-liquid interface increased and the area occupied by each surfactant molecule and the free energies of micellization ΔGm decreased with increasing carbon number (NC) of the hydrophobic chain or decreasing hydrophilicity of counterions, which resulted in a CMC and σcmc decrease, while the log CMC and NC had a linear relationship and a negative correlation. The GFA-QSAR model, which was generated by a training set composed of 13 kinds of alkyl imidazoline though GFA method regression analysis, was highly correlated with predicted values and experimental values of the CMC. The correlation coefficient R was 0.9991, which means high prediction accuracy. The prediction error of 2 kinds of alkyl imidazoline CMCs in the Validation Set that quantitatively analyzed the influence of the alkyl imidazoline molecular structure on the CMC was less than 4%.

  14. Molecular docking and 3D-QSAR studies on triazolinone and pyridazinone, non-nucleoside inhibitor of HIV-1 reverse transcriptase.

    PubMed

    Sivan, Sree Kanth; Manga, Vijjulatha

    2010-06-01

    Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV-1 reverse transcriptase. Recently a series of Triazolinone and Pyridazinone were reported as potent inhibitors of HIV-1 wild type reverse transcriptase. In the present study, docking and 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 31 molecules. Ligands were built and minimized using Tripos force field and applying Gasteiger-Hückel charges. These ligands were docked into protein active site using GLIDE 4.0. The docked poses were analyzed; the best docked poses were selected and aligned. CoMFA and CoMSIA fields were calculated using SYBYL6.9. The molecules were divided into training set and test set, a PLS analysis was performed and QSAR models were generated. The model showed good statistical reliability which is evident from the r2 nv, q2 loo and r2 pred values. The CoMFA model provides the most significant correlation of steric and electrostatic fields with biological activities. The CoMSIA model provides a correlation of steric, electrostatic, acceptor and hydrophobic fields with biological activities. The information rendered by 3D QSAR model initiated us to optimize the lead and design new potential inhibitors.

  15. Understanding the toxicological potential of aerosol organic compounds using informatics based screening

    NASA Astrophysics Data System (ADS)

    Topping, David; Decesari, Stefano; Bassan, Arianna; Pavan, Manuela; Ciacci, Andrea

    2016-04-01

    Exposure to atmospheric particulate matter is responsible for both short-term and long-term adverse health effects. So far, all efforts spent in achieving a systematic epidemiological evidence of specific aerosol compounds determining the overall aerosol toxicity were unsuccessful. The results of the epidemiological studies apparently conflict with the laboratory toxicological analyses which have highlighted very different chemical and toxicological potentials for speciated aerosol compounds. Speciation remains a problem, especially for organic compounds: it is impossible to conduct screening on all possible molecular species. At the same time, research on toxic compounds risks to be biased towards the already known compounds, such as PAHs and dioxins. In this study we present results from an initial assessment of the use of in silico methods (i.e. (Q)SAR, read-across) to predict toxicity of atmospheric organic compounds including evaluation of applicability of a variety of popular tools (e.g. OECD QSAR Toolbox) for selected endpoints (e.g. genotoxicity). Compounds are categorised based on the need of new experimental data for the development of in silico approaches for toxicity prediction covering this specific chemical space, namely the atmospheric aerosols. Whilst only an initial investigation, we present recommendations for continuation of this work.

  16. The 'Toolbox' of strategies for managing Haemonchus contortus in goats: What's in and what's out.

    PubMed

    Kearney, P E; Murray, P J; Hoy, J M; Hohenhaus, M; Kotze, A

    2016-04-15

    A dynamic and innovative approach to managing the blood-consuming nematode Haemonchus contortus in goats is critical to crack dependence on veterinary anthelmintics. H. contortus management strategies have been the subject of intense research for decades, and must be selected to create a tailored, individualized program for goat farms. Through the selection and combination of strategies from the Toolbox, an effective management program for H. contortus can be designed according to the unique conditions of each particular farm. This Toolbox investigates strategies including vaccines, bioactive forages, pasture/grazing management, behavioural management, natural immunity, FAMACHA, Refugia and strategic drenching, mineral/vitamin supplementation, copper Oxide Wire Particles (COWPs), breeding and selection/selecting resistant and resilient individuals, biological control and anthelmintic drugs. Barbervax(®), the ground-breaking Haemonchus vaccine developed and currently commercially available on a pilot scale for sheep, is prime for trialling in goats and would be an invaluable inclusion to this Toolbox. The specialised behaviours of goats, specifically their preferences to browse a variety of plants and accompanying physiological adaptations to the consumption of secondary compounds contained in browse, have long been unappreciated and thus overlooked as a valuable, sustainable strategy for Haemonchus management. These strategies are discussed in this review as to their value for inclusion into the 'Toolbox' currently, and the future implications of ongoing research for goat producers. Combining and manipulating strategies such as browsing behaviour, pasture management, bioactive forages and identifying and treating individual animals for haemonchosis, in addition to continuous evaluation of strategy effectiveness, is conducted using a model farm scenario. Selecting strategies from the Toolbox, with regard to their current availability, feasibility, economical cost

  17. QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity.

    PubMed

    Gomes, Marcelo N; Braga, Rodolpho C; Grzelak, Edyta M; Neves, Bruno J; Muratov, Eugene; Ma, Rui; Klein, Larry L; Cho, Sanghyun; Oliveira, Guilherme R; Franzblau, Scott G; Andrade, Carolina Horta

    2017-09-08

    New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  18. The QSAR and docking calculations of fullerene derivatives as HIV-1 protease inhibitors

    NASA Astrophysics Data System (ADS)

    Saleh, Noha A.

    2015-02-01

    The inhibition of HIV-1 protease is considered as one of the most important targets for drug design and the deactivation of HIV-1. In the present work, the fullerene surface (C60) is modified by adding oxygen atoms as well as hydroxymethylcarbonyl (HMC) groups to form 6 investigated fullerene derivative compounds. These compounds have one, two, three, four or five O atoms + HMC groups at different positions on phenyl ring. The effect of the repeating of these groups on the ability of suggested compounds to inhibit the HIV protease is studied by calculating both Quantitative Structure Activity Relationship (QSAR) properties and docking simulation. Based on the QSAR descriptors, the solubility and the hydrophilicity of studied fullerene derivatives increased with increasing the number of oxygen atoms + HMC groups in the compound. While docking calculations indicate that, the compound with two oxygen atoms + HMC groups could interact and binds with HIV-1 protease active site. This is could be attributed to the active site residues of HIV-1 protease are hydrophobic except the two aspartic acids. So that, the increase in the hydrophilicity and polarity of the compound is preventing and/or decreasing the hydrophobic interaction between the compound and HIV-1 protease active site.

  19. Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

    EPA Science Inventory

    The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

  20. An examination of data quality on QSAR Modeling in regards to the environmental sciences (UNC-CH talk)

    EPA Science Inventory

    The development of QSAR models is critically dependent on the quality of available data. As part of our efforts to develop public platforms to provide access to predictive models, we have attempted to discriminate the influence of the quality versus quantity of data available to...

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