International Journal of Quantitative Structure-Property Relationships · 2020 · 249 citations · 101 references
PharmacyEnvironmental SciencesComputational ChemistryDrug DesignDrug DiscoveryChemical Space ExplorationMolecular ModelingMolecular Property PredictionQuantitative Structure-activity RelationshipsApplicability Domain AnalysisApplicability Domain AssessmentChemometric ModelingExplanatory Variable SelectionGenetic Algorithm Variable SelectionMultiple RegressionOrdinary Least SquaresPhysicochemical Property Modeling
QSAR modeling relies on multiple linear regression with OLS and genetic‑algorithm variable selection, and emphasizes rigorous external validation and applicability‑domain checks to ensure predictive reliability for environmental pollutant prioritization. The author reviews key principles of QSAR modeling, offering expert commentary and recommendations drawn from 23 years of environmental QSAR research. These principles are incorporated into the QSARINS software, providing a legacy tool for the QSAR community.
At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.
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