Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery.

ABSTRACT: Structural similarity metrics such as the Tanimoto coefficient (TC) miss many functionally related compounds-indeed, 60% of similarly bioactive ligand pairs in the ChEMBL database show TC < 0.30, revealing a major blind spot that constrains ligand-based discovery. Our motivation is to overcome this blind spot and enable the recovery of structurally different yet functionally equivalent chemotypes that structure-based similarity fails to detect. Here, we introduce the bioactivity similarity index (BSI), a machine learning model that estimates the probability that two molecules bind the same or related protein receptors. Trained under leave-one-protein-out (LOPO) across Pfam-defined protein groups on dissimilar pairs, BSI not only outperforms TC but also surpasses modern molecular embedding

SUBMITTER: Schottlender G 

PROVIDER: S-EPMC12698616 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature