Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …

Scoring functions are important components in molecular docking for structure-based drug
discovery. Traditional scoring functions, generally empirical-or force field-based, are robust …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

Molecular docking, scoring, and virtual screening play an increasingly important role in
computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the …

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

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 …

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 …

Accurate and affordable assessment of ligand–protein affinity for structure-based virtual
screening (SB-VS) is a standing challenge. Hence, empirical postdocking filters making use …

The development of new protein–ligand scoring functions using machine learning
algorithms, such as random forest, has been of significant interest. By efficiently utilizing …

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