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Principles of QSAR Modeling

Paola Gramatica

2020International Journal of Quantitative Structure-Property Relationships249 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

Quantitative structure–activity relationshipApplicability domainComputer scienceChemometricsFeature selectionReliability (semiconductor)Machine learningPartial least squares regressionArtificial intelligencePower (physics)PhysicsQuantum mechanicsComputational Drug Discovery MethodsSpectroscopy and Chemometric AnalysesMetabolomics and Mass Spectrometry Studies
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