Computer-aided drug discovery/design methods have played a major role in the
development of therapeutically important small molecules for over three decades. These …

Virtual filtering and screening of combinatorial libraries have recently gained attention as
methods complementing the high-throughput screening and combinatorial chemistry. These …

Progress in medicinal chemistry and in drug design depends on our ability to understand the
interactions of drugs with their biological targets. Classical QSAR studies describe biological …

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 …

Antioxidative peptides have attracted increasing attention from researchers because of their
antioxidant properties as natural materials in functional food and for applications in …

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 …

A large number of methods are available for modeling quantitative structure− activity
relationships (QSAR). We examine the predictive accuracy of several methods applied to …

Quantitative structure–activity relationships (QSAR) is an area of computational research
that builds virtual models to predict quantities such as the binding affinity or the toxic …

Lipophilicity is a major determinant of pharmacokinetic and pharmacodynamic properties of
drug molecules. Correspondingly, there is great interest in medicinal chemistry in …

Abstract Anti-Microbial Peptide Database version 1 (AMPDB v1) is a meticulously curated
resource that aims to address the limitations of existing databases in the field of …