6533b7cffe1ef96bd1258f21

RESEARCH PRODUCT

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

Tomasz ArodzArkadiusz Z. DudekJorge Galvez

subject

Models MolecularQuantitative structure–activity relationshipbusiness.industryComputer scienceOrganic ChemistryQuantitative Structure-Activity RelationshipQuantitative structureFeature selectionGeneral MedicineMachine learningcomputer.software_genreCombinatorial chemistryField (computer science)Computer Science ApplicationsDomain (software engineering)Molecular descriptorDrug DiscoveryArtificial intelligencebusinesscomputerApplicability domain

description

Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.

https://doi.org/10.2174/138620706776055539