Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Accelerating growth and global expansion of antimicrobial resistance has deepened the
need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear …

Drug discovery is a rigorous process that requires billion dollars of investments and decades
of research to bring a molecule “from bench to a bedside”. While virtual docking can …

In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical
structures of contaminants and develop machine learning (ML)-based predictive models for …

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past
decade. Various AI techniques have been used in many drug discovery applications, such …

Highlights•Drug discovery data and data from other sources are different in quantity and
characteristics.•This article underlines the difference of data from different domains.•In order …

Machine learning (ML) models to predict the toxicity of small molecules have garnered great
attention and have become widely used in recent years. Computational toxicity prediction is …

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be
used to predict toxicity of various chemical compounds or classify the compounds based on …

Drug toxicity evaluation is an essential process of drug development as it is reportedly
responsible for the attrition of approximately 30% of drug candidates. The rapid increase in …

The identification of physical interactions between drug candidate compounds and target
biomolecules is an important process in drug discovery. Since conventional screening …