Protein-protein interactions (PPIs) play a critical role in various biological processes.
Accurately estimating the binding affinity of PPIs is essential for understanding the …

Identifying novel drug–target interactions (DTIs) plays an important role in drug discovery.
Most of the computational methods developed for predicting DTIs use binary classification …

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 …

Motivation The prediction of binding affinity between drug and target is crucial in drug
discovery. However, the accuracy of current methods still needs to be improved. On the …

Binding affinity prediction largely determines the discovery efficiency of lead compounds in
drug discovery. Recently, machine learning (ML)-based approaches have attracted much …

Scoring functions for the prediction of protein-ligand binding affinity have seen renewed
interest in recent years when novel machine learning and deep learning methods started to …

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial
role in protein engineering and drug design. In this study, we develop GeoPPI, a novel …

Molecular docking enables large-scale prediction of whether and how small molecules bind
to a macromolecular target. Machine-learning scoring functions are particularly well suited to …

Identification of high affinity drug‐target interactions is a major research question in drug
discovery. Proteins are generally represented by their structures or sequences. However …

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