{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:25:24Z","timestamp":1787570724185,"version":"build-2736575974"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/K004131\/1"],"award-info":[{"award-number":["BB\/K004131\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/F00964X\/1"],"award-info":[{"award-number":["BB\/F00964X\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/M025047\/1"],"award-info":[{"award-number":["BB\/M025047\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/T001070\/1"],"award-info":[{"award-number":["MR\/T001070\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013704","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda Paraguay","doi-asserted-by":"crossref","award":["PINV01-719"],"award-info":[{"award-number":["PINV01-719"]}],"id":[{"id":"10.13039\/501100013704","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013704","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda Paraguay","doi-asserted-by":"crossref","award":["PINV01-108"],"award-info":[{"award-number":["PINV01-108"]}],"id":[{"id":"10.13039\/501100013704","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"crossref","award":["311181\/2022-8"],"award-info":[{"award-number":["311181\/2022-8"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004586","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio de Janeiro","doi-asserted-by":"crossref","award":["313268\/2026-6"],"award-info":[{"award-number":["313268\/2026-6"]}],"id":[{"id":"10.13039\/501100004586","id-type":"DOI","asserted-by":"crossref"}]},{"name":"College Studentship from RHUL"},{"name":"Roy Edwards Scholarship"},{"DOI":"10.13039\/100016883","name":"Funda\u00e7\u00e3o Getulio Vargas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016883","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Identifying drug targets is fundamental in drug development, both for discovering new therapies and for ensuring effective and safe treatments. Drug-target interactions (DTIs) have been predicted using machine learning approaches that integrate heterogeneous data; however, often these models are complex and lack interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We investigate whether a simple, fully interpretable linear model can achieve competitive performance for DTI prediction. We propose Linear Interpretable Drug-Target Interaction (LI-DTI), a prediction model inspired by recommender systems. LI-DTI learns from different drug-drug and target-target similarity matrices and provides interpretable predictions as a linear combination of these similarity measures. We show that LI-DTI can recover DTIs even when drugs or targets have no previously known interactions, across multiple cross-validation settings. We further evaluate performance while mitigating potential bias arising from high chemical similarity between drugs or sequence similarity between targets. Finally, we assess LI-DTI in a prospective evaluation, training on DTIs present in DrugBank from 2011 and testing on interactions added through 2022. Across all evaluations, LI-DTI achieves state-of-the-art performance while producing interpretable predictions. For practical use, we provide a web-based tool that enables users to visualize individual LI-DTI predictions for DrugBank (2025) and inspect the biological evidence underlying them. Our results indicate that simple linear models with well-curated similarity features can deliver robust and interpretable DTI predictions, facilitating hypothesis generation and downstream experimental prioritization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Code and data available at https:\/\/github.com\/paccanarolab\/LI-DTI. Web tool available at https:\/\/paccanarolab.org\/lidtiweb\/.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag428","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:50:03Z","timestamp":1782993003000},"source":"Crossref","is-referenced-by-count":0,"title":["A linear interpretable model for drug target prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Santiago","family":"Noto","sequence":"first","affiliation":[{"name":"School of Applied Mathematics, Funda\u00e7\u00e3o Getulio Vargas , Rio de Janeiro, 22250-900,","place":["Brazil"]},{"name":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London , Egham Hill, Egham, TW20 0EX,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6958-7763","authenticated-orcid":false,"given":"Santiago","family":"Ferreyra","sequence":"additional","affiliation":[{"name":"School of Applied Mathematics, Funda\u00e7\u00e3o Getulio Vargas , Rio de Janeiro, 22250-900,","place":["Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8577-351X","authenticated-orcid":false,"given":"Ruben","family":"Jimenez","sequence":"additional","affiliation":[{"name":"School of Applied Mathematics, Funda\u00e7\u00e3o Getulio Vargas , Rio de Janeiro, 22250-900,","place":["Brazil"]},{"name":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London , Egham Hill, Egham, TW20 0EX,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1748-7148","authenticated-orcid":false,"given":"Diego","family":"Galeano","sequence":"additional","affiliation":[{"name":"School of Applied Mathematics, Funda\u00e7\u00e3o Getulio Vargas , Rio de Janeiro, 22250-900,","place":["Brazil"]},{"name":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London , Egham Hill, Egham, TW20 0EX,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8059-1346","authenticated-orcid":false,"given":"Alberto","family":"Paccanaro","sequence":"additional","affiliation":[{"name":"School of Applied Mathematics, Funda\u00e7\u00e3o Getulio Vargas , Rio de Janeiro, 22250-900,","place":["Brazil"]},{"name":"Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London , Egham Hill, Egham, 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computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"key":"2026082407023413000_btag428-B9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"Neodti: neural integration of neighbor information from a heterogeneous network for discovering new drug\u2013target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"2026082407023413000_btag428-B10","doi-asserted-by":"crossref","first-page":"bbac184","DOI":"10.1093\/bib\/bbac184","article-title":"A heterogeneous network-based method with attentive meta-path extraction for predicting drug\u2013target interactions","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026082407023413000_btag428-B11","doi-asserted-by":"crossref","first-page":"6775","DOI":"10.1038\/s41467-021-27137-3","article-title":"A unified drug\u2013target interaction prediction framework based on knowledge graph and recommendation system","volume":"12","author":"Ye","year":"2021","journal-title":"Nat 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