{Reference Type}: Journal Article
{Title}: FitScore: a fast machine learning-based score for 3D virtual screening enrichment.
{Author}: Gehlhaar DK;Mermelstein DJ;
{Journal}: J Comput Aided Mol Des
{Volume}: 38
{Issue}: 1
{Year}: 2024 Aug 16
{Factor}: 4.179
{DOI}: 10.1007/s10822-024-00570-4
{Abstract}: Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.