Docking scoring functions are notoriously weak predictors of binding affinity. They typically
assign a common set of weights to the individual energy terms that contribute to the overall …

A dataset of protein‐drug complexes with experimental binding energy and crystal structure
were analyzed and the performance of different docking engines and scoring functions (as …

The field of machine learning for drug discovery is witnessing an explosion of novel
methods. These methods are often benchmarked on simple physicochemical properties …

Training machine learning algorithms with protein–ligand descriptors has recently gained
considerable attention to predict binding constants from atomic coordinates. Starting from a …

Objective The objective of this paper is to highlight the state-of-the-art machine learning (ML)
techniques in computational docking. The use of smart computational methods in the life …

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 …

Molecular docking is widely used to predict novel lead compounds for drug discovery.
Success depends on the quality of the docking scoring function, among other factors. An …

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 …

Molecular docking is a widely used technique in drug design to predict the binding pose of a
candidate compound in a defined therapeutic target. Numerous docking protocols are …

Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …