The goal of a quantitative structure–activity relationship (QSAR) model is to encode the
relationship between molecular structure and biological activity or physical property. Based …

This paper is an overview of the most significant and impactful interpretation approaches of
quantitative structure–activity relationship (QSAR) models, their development, and …

This perspective concerns the methods employed within the current drug discovery
community to develop predictive quantitative structure–activity relationships (QSAR) …

Feature selection is one of the most commonly used and reliable methods for deriving
predictive quantitative structure–activity relationships (QSAR). Many feature selection …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Publisher Summary Medicinal chemists attempt to develop qualitative structure–activity
relationships (SAR) for a synthesized series of compounds in a drug discovery project which …

Key requirements for quantitative structure–activity relationship (QSAR) models to gain
acceptance by regulatory authorities include a defined domain of applicability (DA) and …

Abstract Quantitative Structure–Activity Relationship (QSAR) models are used increasingly
to screen chemical databases and/or virtual chemical libraries for potentially bioactive …

Publisher Summary In drug and agrochemical discovery it is of crucial importance to
understand the relationship between chemical structure and biological activity. The …

This paper describes the Structural and Physico-Chemical Interpretation (SPCI) approach,
which is an extension of a recently reported method for interpretation of quantitative structure …