Docking tools to predict whether and how a small molecule binds to a target can be applied
if a structural model of such target is available. The reliability of docking depends, however …

Predicting the binding affinities of large sets of diverse molecules against a range of
macromolecular targets is an extremely challenging task. The scoring functions that attempt …

There is a growing body of evidence showing that machine learning regression results in
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …

Classical scoring functions have reached a plateau in their performance in virtual screening
and binding affinity prediction. Recently, machine-learning scoring functions trained on …

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 …

Docking scoring functions can be used to predict the strength of protein-ligand binding. It is
widely believed that training a scoring function with low-quality data is detrimental for its …

New empirical scoring functions have been developed to estimate the binding affinity of a
given protein-ligand complex with known three-dimensional structure. These scoring …

In virtual screening, small-molecule ligands are docked into protein binding sites and their
binding affinity is predicted. Knowledge-based, regression-based and first-principle-based …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

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 …