The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …

In recent years, machine-learning-based scoring functions have significantly improved the
scoring power. However, many of these methods do not perform well in distinguishing the …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …

Abstract Structure-based drug design depends on the detailed knowledge of the three-
dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of …

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

Background Molecular docking is a widely-employed method in structure-based drug
design. An essential component of molecular docking programs is a scoring function (SF) …

The expertise accumulated in deep neural network-based structure prediction has been
widely transferred to the field of protein–ligand binding pose prediction, thus leading to the …

In recent years, the cheminformatics community has seen an increased success with
machine learning-based scoring functions for estimating binding affinities and pose …

Modeling the interaction between proteins and ligands and accurately predicting their
binding structures is a critical yet challenging task in drug discovery. Recent advancements …

Scoring and numerical optimization of protein–ligand poses is an integral part of docking
tools. Although many scoring functions exist, many of them are not continuously …