Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

Understanding the interactions formed between a ligand and its molecular target is key to
guiding the optimization of molecules. Different experimental and computational methods …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …

Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …

Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to
deliver across the drug discovery and development value chain, starting from target …

The recent pandemic has emphasized the need for new drugs. However, taking an idea for
a drug through the discovery pipeline to the clinic is a long, complex, and expensive …

Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Computationally identifying new targets for existing drugs has drawn much attention in drug
repurposing due to its advantages over de novo drugs, including low risk, low costs, and …