In drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a
pivotal task for lead optimization with acceptable on-target potency as well as …

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to
computer-aided drug design, but remains a challenging problem. To achieve efficient and …

We propose a novel machine-learning-based scoring function for drug discovery that
incorporates ligand and protein structural information into a knowledge-based PMF score …

Abstract Motivation Fingerprints (FPs) are the most common small molecule representation
in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity …

As a key element in structure-based drug design, binding affinity prediction (BAP) for
putative protein-ligand complexes can be efficiently achieved by the incorporation of …

Motivation Reliable predictive models of protein–ligand binding affinity are required in many
areas of biomedical research. Accurate prediction based on current descriptors or molecular …

Discovery and optimization of small molecule inhibitors as therapeutic drugs have
immensely benefited from rational structure-based drug design. With recent advances in …

Motivation Machine-learning scoring functions (SFs) have been found to outperform
standard SFs for binding affinity prediction of protein–ligand complexes. A plethora of …

Protein--ligand interaction (PLI) prediction is critical in drug discovery, aiding the
identification and enhancement of molecules that effectively bind to target proteins. Despite …

Highlights•Interaction fingerprints (IFP) have been effectively used for GPCR ligand
screening.•Machine learning enables IFP and bioactivity data integration.•Binding site …