Quantitative Structure-Activity Relationship (QSAR) is based on the hypothesis that changes
in molecular structure reflect changes in the observed response or biological activity. The …

Quantitative Structure-Activity Relationships (QSAR) are based on the hypothesis that
changes in molecular structure reflect proportional changes in the observed response or …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

In a review of structure-property correlations in molecular design, 1 the relevance today of
what is arguably one of the earliest publications2 on QSAR was pointed out. In this paper …

Drug design is a process which is driven by technological breakthroughs implying advanced
experimental and computational methods. Nowadays, the techniques or the drug design …

Introduction: Quantitative structure–activity relationship (QSAR) modeling is one of the most
popular computer-aided tools employed in medicinal chemistry for drug discovery and lead …

Virtual screening can accelerate drug discovery by identifying promising candidates for
experimental evaluation. Machine learning is a powerful method for screening, as it can …

Drug design is a process driven by innovation and technological breakthroughs involving a
combination of advanced experimental and computational methods. A broad variety of …

Abstract 3D-QSAR is typically used to construct models (1) to predict activities,(2) to illustrate
significant regions, and (3) to provide insight into possible interactions. To the contrary …

Numerous regression-based and machine learning techniques are available for the
development of linear and nonlinear QSAR models that can accurately predict biological …