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 …

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 …

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 …

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

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …

Scoring functions for protein–ligand interactions play a critical role in structure-based drug
design. Owing to the good balance between general applicability and computational …

Background Current scoring functions are not very successful in protein-ligand binding
affinity prediction albeit their popularity in structure-based drug designs. Here, we propose a …

Accurately predicting the binding affinities of large diverse sets of protein-ligand complexes
efficiently is a key challenge in computational biomolecular science, with applications in …

Accurately predicting the binding affinities of large sets of protein-ligand complexes
efficiently is a key challenge in computational biomolecular science, with applications in …