{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T06:39:07Z","timestamp":1776580747788,"version":"3.51.2"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>\n                    Structural similarity metrics such as the Tanimoto coefficient (TC) miss many functionally related compounds\u2014indeed, 60% of similarly bioactive ligand pairs in the ChEMBL database show TC &amp;lt; 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 baselines (ChemBERTa and contrastive language-molecule pre-training (CLAMP), using cosine similarity) across protein families. We further develop a cross-family model (BSI-Large) that, while slightly below group-specific models, generalizes better and can be fine-tuned with less data, consistently improving over models trained from scratch. In retrospective validation on new ChEMBL v35 data, BSI achieves strong early-retrieval performance (top 2% enrichment factor, EF\n                    <jats:sub>2%<\/jats:sub>\n                    ), with group-specific models delivering the best enrichment, and BSI-Large remaining competitive. In a realistic virtual screening-like scenario against the target gene ADRA2B, the mean rank of the next active, given a known active, improves from 45.2 (TC) to 3.9 (BSI), with 54.9 for ChemBERTa and 28.6 for CLAMP. Altogether, BSI complements, rather than replaces, structure-based similarity and embedding-based comparisons, extending hit finding to remote chemotypes that are structurally dissimilar yet functionally equivalent. The code is available at\n                    <jats:ext-link>https:\/\/github.com\/gschottlender\/bioactivity-similarity-index<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.3389\/fbinf.2025.1695353","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T06:29:42Z","timestamp":1764311382000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery"],"prefix":"10.3389","volume":"5","author":[{"given":"Gustavo","family":"Schottlender","sequence":"first","affiliation":[]},{"given":"Juan Manuel","family":"Prieto","sequence":"additional","affiliation":[]},{"given":"Marcelo A.","family":"Marti","sequence":"additional","affiliation":[]},{"given":"Dario","family":"Fern\u00e1ndez Do Porto","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"113662","DOI":"10.1016\/j.eswa.2020.113662","article-title":"Incorporating part-whole hierarchies into fully convolutional network for scene parsing","volume":"160","author":"Abbasi","year":"2020","journal-title":"Expert Systems Applications"},{"key":"B2","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"B3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s13321-025-00999-1","article-title":"InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem","volume":"17","author":"An","year":"2025","journal-title":"J. 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