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

Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …

Background In recent years, since the molecular docking technique can greatly improve the
efficiency and reduce the research cost, it has become a key tool in computer-assisted drug …

Computer-aided protein–ligand binding predictions are a valuable help in drug discovery.
Protein–ligand docking programs generally consist of two main components: a scoring …

Molecular docking can be used to predict how strongly small‐molecule binders and their
chemical derivatives bind to a macromolecular target using its available three‐dimensional …

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 …

Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving
novel hit compounds in drug discovery. However, the accuracy of the current docking …

Motivation Studies have shown that the accuracy of random forest (RF)-based scoring
functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of …

The last few years have seen the development of numerous deep learning-based protein–
ligand docking methods. They offer huge promise in terms of speed and accuracy. However …

Docking programs are widely used to discover novel ligands efficiently and can predict
protein− ligand complex structures with reasonable accuracy and speed. However, there is …