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

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

A pervasive challenge in drug design is determining how to expand a ligand─ a small
molecule that binds to a target biomolecule─ in order to improve various properties of the …

Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps
in understanding the degree of protein-ligand interactions and is a useful measure in drug …

Empirical scoring functions based on either molecular force fields or cheminformatics
descriptors are widely used, in conjunction with molecular docking, during the early stages …

Ligand-based drug design has recently benefited from the development of deep generative
models. These models enable extensive explorations of the chemical space and provide a …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …