The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …

Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total
infections is still rising. It is necessary to identify some potentially effective drugs that can be …

Identifying native-like protein–ligand complexes (PLCs) from an abundance of docking
decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Drug discovery and development process is very lengthy, highly expensive and extremely
complex in nature. Traditional methods involve expensive techniques and take many years …

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery.
Computerized virtual screening techniques have been used for DTA prediction, greatly …

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug
discovery. While deep learning models have been proposed to accelerate the identification …

Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …