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

Our comparative assessment of scoring functions (CASF) benchmark is created to provide
an objective evaluation of current scoring functions. The key idea of CASF is to compare the …

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

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 …

Scoring functions are often applied in combination with molecular docking methods to
predict ligand binding poses and ligand binding affinities or to identify active compounds …

In structure-based drug design, scoring functions are often employed to evaluate protein–
ligand interactions. A variety of scoring functions have been developed so far, and thus …

In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …

Scoring functions have been widely used to assess protein–ligand binding affinity in
structure-based drug discovery. However, currently commonly used scoring functions face …

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 …

The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …