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 …

Computational methods have gained prominence in healthcare research. The accessibility
of healthcare data has greatly incited academicians and researchers to develop executions …

Recently, effective and rapid deep-learning methods for predicting chemical reactions have
significantly aided the research and development of organic chemistry and drug discovery …

Drug discovery is a complex and iterative process, making it ideal for using artificial
intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying …

Deregulation of epidermal growth factor receptors is one of the major causes of lung
cancers, and its kinase has been targeted in associated therapy. Often, mutations causing …

In drug discovery and combination therapy, drug-drug interactions can lead to adverse
reactions, affecting not only disease treatment but also risking the market withdrawal of new …

Drug discovery is said to be a multi-dimensional issue in which different properties of drug
candidates including efficacy, pharmacokinetics, and safety need to be improved with …

Natural Language Processing (NLP) has revolutionized the way computers are used to
study and interact with human languages and is increasingly influential in the study of …

Conformations of kinase obtained from molecular dynamic (MD) simulation plays an
important role in molecular docking experiment in the field of drug discovery and …

Artificial intelligence, or AI, is a technology that can interpret and learn from data it is fed to
make decisions for a certain function independent of human control 1. For example, some AI …