Journal of Computational Chemistry · 2013 · 766 citations · 27 references
PharmacyPharmacologyToxicologyDrug DesignDrug DiscoveryStructure-activity RelationshipsChemical Space ExplorationComputational ToxicologyMolecular DescriptorsMolecular Property PredictionQuantitative Structure-activity RelationshipsStructure-activity Relationship AnalysisChemical Structure-activity RelationshipsCheminformatics ModelingComputer-aided Drug DiscoveryExplanatory Variable SelectionGenetic Algorithm Variable SelectionModel Predictivity AssessmentOrdinary Least SquaresStructure-activity Relationship Modeling
QSARINS is a user‑friendly software that develops and validates multiple‑linear‑regression QSAR models using OLS and genetic‑algorithm variable selection, aligning with OECD principles and enabling reliability analysis. The software implements PCA, descriptor pre‑reduction, dataset splitting, outlier detection, interpolation/extrapolation, internal and external validation, consensus modeling, and visualization tools, and supports validation of models built with other programs. It includes an Insubria PBT index model that predicts the cumulative behavior of new chemicals as persistent, bioaccumulative, and toxic. © 2013 Wiley Periodicals, Inc.
QSARINS (QSAR‐INSUBRIA) is a new software for the development and validation of multiple linear regression Quantitative Structure‐Activity Relationship (QSAR) models by Ordinary Least Squares method and Genetic Algorithm for variable selection. This program is mainly focused on the external validation of QSAR models. Various tools for explorative analysis of the datasets by Principal Component Analysis, prereduction of input molecular descriptors, splitting of datasets in training and prediction sets, detection of outliers and interpolated or extrapolated predictions, internal and external validation by different parameters, consensus modeling and various plots for visualizations are implemented. QSARINS is a user‐friendly platform for QSAR modeling in agreement with the OECD Principles and for the analysis of the reliability of the obtained predicted data. The Insubria Persistent Bioaccumulative and Toxic (PBT) Index model for the prediction of the cumulative behavior of new chemicals as PBTs is implemented. Additionally, QSARINS allows the user to validate single models, predeveloped using also different software. © 2013 Wiley Periodicals, Inc.
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