Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

A typical drug discovery project involves identifying active compounds with significant
binding potential for selected disease-specific targets. Experimental high-throughput …

Abstract Machine Learning for ligand based virtual screening (LB-VS) is an important in-
silico tool for discovering new drugs in a faster and cost-effective manner, especially for …

The interplay between life sciences and advancing technology drives a continuous cycle of
chemical data growth; these data are most often stored in open or partially open databases …

While the use of machine-learning (ML) techniques is well established in cheminformatics
for the prediction of physicochemical properties and binding affinities, the training of ML …

With the dramatic development of high-performance computing, the emergence of better
algorithms and the accumulation of large amounts of chemical and biological data, computer …

The discovery paradigm of drugs is rapidly growing due to advances in machine learning
(ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug …

CNS disorders are indications with a very high unmet medical needs, relatively smaller
number of available drugs, and a subpar satisfaction level among patients and caregiver …

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due
to substantial progress in high-performance computing, the development of superior …

Background: Automated compound testing is currently the de facto standard method for drug
screening, but it has not brought the great increase in the number of new drugs that was …