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

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 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 …

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 …

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 …

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 …

Automated docking is one of the most important tools for structure‐based drug design that
allows prediction of ligand binding poses and also provides an estimate of how well small …

Autodock Vina is a very popular, and highly cited, open source docking program. Here we
present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based …

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

There is a growing body of evidence showing that machine learning regression results in
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …