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

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 …

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

Prediction of protein–ligand binding affinities is a central issue in structure-based computer-
aided drug design. In recent years, much effort has been devoted to the prediction of the …

The ability to rapidly assess the quality of a protein‐ligand complex in terms of its affinity is of
fundamental importance for various methods of computer‐aided drug design. While simple …

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 …

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 …

Molecular docking, scoring, and virtual screening play an increasingly important role in
computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the …

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 …

Scoring functions are routinely deployed in structure-based drug design to quantify the
potential for protein–ligand (PL) complex formation. Here, we present a new scoring function …