{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:48:18Z","timestamp":1791874098168,"version":"4.3.4"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Designing compounds capable of modulating multiple target proteins simultaneously (i.e., polypharmacology) is critical for treating complex diseases like cancer and neurodegenerative disorders. However, AI-driven multi-target drug design remains severely hindered by the scarcity of multi-target 3D structural data and the inefficiency of traditional \u201cgenerate-then-filter\u201d pipelines. There is a pressing need for scalable methods that can satisfy diverse binding constraints across multiple proteins without relying on scarce structural priors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To overcome these bottlenecks, we reformulate multi-target drug design as a sequence-based generation task and introduce PolyTamGen, an end-to-end autoregressive framework. We first curate a large-scale ligand-centric bioactivity dataset from ChEMBL and BindingDB, comprising 1.6 million unique ligands and 4.9 million bioactivity entries. PolyTamGen encodes target protein sets using ESM3, compresses these high-dimensional features into compact tokens via an attention module, and feeds them into a sequence-based compound generator to produce the final multi-target ligands. We further apply Direct Preference Optimization (DPO) to improve docking-based affinity proxies and drug-likeness. Extensive experiments on dual- and triple-target benchmarks demonstrate that PolyTamGen improves joint success rates over representative baselines while maintaining high chemical validity and diversity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code is available at https:\/\/github.com\/bjzgcai\/PolyTamGen. The processed data, model checkpoints, and benchmark results are available at Zenodo: https:\/\/doi.org\/10.5281\/zenodo.20594128.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag766","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:47:47Z","timestamp":1791632867000},"source":"Crossref","is-referenced-by-count":0,"title":["Polypharmacological Target-Aware Molecule Generation"],"prefix":"10.1093","author":[{"given":"Kehan","family":"Wu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China , Hefei, Anhui China"},{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , Hefei, Anhui China"},{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Guo","sequence":"additional","affiliation":[{"name":"Global Health Drug Discovery Institute , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renhe","family":"Liu","sequence":"additional","affiliation":[{"name":"Global Health Drug Discovery Institute , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shi","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiran","family":"Jin","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiguang","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China , Hefei, Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Qin","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zequn","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingce","family":"Xia","sequence":"additional","affiliation":[{"name":"Zhongguancun Academy , Beijing, 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,12]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag766\/71892478\/btag766.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag766\/71892478\/btag766.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:06:31Z","timestamp":1791871591000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article\/doi\/10.1093\/bioinformatics\/btag766\/8911832"}},"subtitle":[],"editor":[{"given":"Daisuke","family":"Kihara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag766","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,12]]},"article-number":"btag766"}}