Abstract Machine learning (ML) is a promising approach for predicting small molecule
properties in drug discovery. Here, we provide a comprehensive overview of various ML …

Computing binding affinities is of great importance in drug discovery pipeline and its
prediction using advanced machine learning methods still remains a major challenge as the …

Large language models have greatly enhanced our ability to understand biology and
chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and …

The molecular force field (FF) determines the accuracy of molecular dynamics (MD) and is
one of the major bottlenecks that limits the application of MD in molecular design. Recently …

We subject a series of five protein–ligand systems which contain important SARS-CoV-2
targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose …

Free energy calculations for protein–ligand complexes have become widespread in recent
years owing to several conceptual, methodological and technological advances. Central …

MISATO, a dataset for structure-based drug discovery combines quantum mechanics
property data and molecular dynamics simulations on~ 20,000 protein–ligand structures …

In the field of computational physics and material science, the efficient sampling of rare
events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a …

Cordycepin, or 3′-deoxyadenosine, is an adenosine analog with a broad spectrum of
biological activity. The key structural difference between cordycepin and adenosine lies in …

In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a
powerful tool for predicting binding affinities, estimating transport properties, and exploring …