Protein–ligand docking has been playing an important role in modern drug discovery. To
model drug–target binding in real systems, a number of flexible-ligand docking algorithms …

Computational drug design relies on the calculation of binding strength between two
biological counterparts especially a chemical compound, ie, a ligand, and a protein …

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 …

Structure-based drug design is critically dependent on accuracy of molecular docking
scoring functions, and there is of significant interest to advance scoring functions with …

Ligand docking is one of the core technologies in structure-based virtual screening for drug
discovery. However, conventional docking tools and existing deep learning tools may suffer …

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 …

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

As one of the most popular computational approaches in modern structure-based drug
design, molecular docking can be used not only to identify the correct conformation of a …

Accurately predicting protein–ligand binding affinities is an important problem in
computational chemistry since it can substantially accelerate drug discovery for virtual …

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 …