The molecular docking simulation is a key computational tool in modern drug discovery
research that its predictive performance strongly depends on the employed scoring …

In recent years, thanks to advances in computer hardware and dataset availability, data‐
driven approaches (like machine learning) have become one of the essential parts of the …

The protein–ligand scoring function plays an important role in computer-aided drug
discovery and is heavily used in virtual screening and lead optimization. In this study, we …

Currently, molecular docking is becoming a key tool in drug discovery and molecular
modeling applications. The reliability of molecular docking depends on the accuracy of the …

Computational drug discovery provides an efficient tool for helping large-scale lead
molecule screening. One of the major tasks of lead discovery is identifying molecules with …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, several deep learning …

Scoring functions for protein–ligand interactions play a critical role in structure-based drug
design. Owing to the good balance between general applicability and computational …

Fourteen popular scoring functions, ie, X-Score, DrugScore, five scoring functions in the
Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring …

Accurate determination of protein–ligand binding affinity is a fundamental problem in
biochemistry useful for many applications including drug design and protein–ligand docking …

Protein–ligand interaction prediction presents a significant challenge in drug design.
Numerous machine learning and deep learning (DL) models have been developed to …