Virtual
screening is a promising method for obtaining novel hit
compounds in drug discovery. It aims to enrich potentially active
compounds from a large chemical library for further biological experiments.
However, the accuracy of current virtual screening methods is insufficient.
In this study, we develop a new virtual screening method named Similarity
of Interaction Energy VEctor Score (SIEVE-Score), in which protein–ligand
interaction energies are extracted to represent docking poses for
machine learning. SIEVE-Score offers substantial improvements compared
to other state-of-the-art virtual screening methods, namely, other
machine-learning-based scoring functions, interaction fingerprints,
and docking software, for the enrichment factor 1% results on the
Directory of Useful Decoys, Enhanced (DUD-E). The screening results
are also human-interpretable in the form of important interactions
for distinguishing between active and inactive compounds. The source
code is available at .