Highlights•Chemical descriptors in modeling drug-target interaction.•Modeling approaches
in drug-target interaction prediction.•Machine learning and deep learning models in drug …

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

Background Molecular descriptors and fingerprints have been routinely used in QSAR/SAR
analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and …

abstract Introduction: The use of computational tools in the early stages of drug development
has increased in recent decades. Machine learning (ML) approaches have been of special …

Many chemicals that disrupt endocrine function have been linked to a variety of adverse
biological outcomes. However, screening for endocrine disruption using in vitro or in vivo …

Background With the increasing development of biotechnology and informatics technology,
publicly available data in chemistry and biology are undergoing explosive growth. Such …

The identification of drug/compound–target interactions (DTIs) constitutes the basis of drug
discovery, for which computational predictive approaches have been developed. As a …

Deep learning architectures have proved versatile in a number of drug discovery
applications, including the modeling of in vitro compound activity. While controlling for …

Proteochemometric (PCM) modelling is a computational method to model the bioactivity of
multiple ligands against multiple related protein targets simultaneously. Hence it has been …

Aim: Computational chemogenomics models the compound–protein interaction space,
typically for drug discovery, where existing methods predominantly either incorporate …