One key task in virtual screening is to accurately predict the binding affinity (delta-G) of
protein-ligand complexes. Recently, deep learning (DL) has significantly increased the …

Background Ligand-binding proteins play key roles in many biological processes.
Identification of protein-ligand binding residues is important in understanding the biological …

Exiting computational models for drug–target binding affinity prediction have much room for
improvement in prediction accuracy, robustness and generalization ability. Most deep …

Motivation: Hydrophobic effect plays a pivotal role in most protein–ligand binding. State-of-
the-art protein–ligand scoring methods usually treat hydrophobic free energy as surface …

Motivation The knowledge of potentially druggable binding sites on proteins is an important
preliminary step toward the discovery of novel drugs. The computational prediction of such …

Abstract Information regarding the residue–residue distance between interacting proteins is
important for modelling the structures of protein complexes, as well as being valuable for …

Structural insight of the protein–protein interaction (PPI) interface can provide knowledge
about the kinetics, thermodynamics and molecular functions of the complex while …

Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …

Proteins often interact with each other and form protein complexes to carry out various
biochemical activities. Knowledge of the interaction sites is helpful for understanding …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …