{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:20:54Z","timestamp":1783938054164,"version":"3.55.0"},"reference-count":72,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","award":["Startup Grant 314 M4081842"],"award-info":[{"award-number":["Startup Grant 314 M4081842"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Singapore Ministry of Education Academic Research fund","award":["Tier 1 RG109\/19, MOE-T2EP20120-0013 and MOE-T2EP20220-0010"],"award-info":[{"award-number":["Tier 1 RG109\/19, MOE-T2EP20120-0013 and MOE-T2EP20220-0010"]}]},{"DOI":"10.13039\/501100018769","name":"Nankai Zhide Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018769","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NSFC grant no. 11931007, 11221091, 11271062, 11571184"],"award-info":[{"award-number":["NSFC grant no. 11931007, 11221091, 11271062, 11571184"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["NSFC grant no. 11971144"],"award-info":[{"award-number":["NSFC grant no. 11971144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"High-level Scientific Research Foundation of Hebei Province"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>With the great advancements in experimental data, computational power and learning algorithms, artificial intelligence (AI) based drug design has begun to gain momentum recently. AI-based drug design has great promise to revolutionize pharmaceutical industries by significantly reducing the time and cost in drug discovery processes. However, a major issue remains for all AI-based learning model that is efficient molecular representations. Here we propose Dowker complex (DC) based molecular interaction representations and Riemann Zeta function based molecular featurization, for the first time. Molecular interactions between proteins and ligands (or others) are modeled as Dowker complexes. A multiscale representation is generated by using a filtration process, during which a series of DCs are generated at different scales. Combinatorial (Hodge) Laplacian matrices are constructed from these DCs, and the Riemann zeta functions from their spectral information can be used as molecular descriptors. To validate our models, we consider protein-ligand binding affinity prediction. Our DC-based machine learning (DCML) models, in particular, DC-based gradient boosting tree (DC-GBT), are tested on three most-commonly used datasets, i.e., including PDBbind-2007, PDBbind-2013 and PDBbind-2016, and extensively compared with other existing state-of-the-art models. It has been found that our DC-based descriptors can achieve the state-of-the-art results and have better performance than all machine learning models with traditional molecular descriptors. Our Dowker complex based machine learning models can be used in other tasks in AI-based drug design and molecular data analysis.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009943","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T17:36:01Z","timestamp":1649266561000},"page":"e1009943","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":34,"title":["Dowker complex based machine learning (DCML) models for protein-ligand binding affinity prediction"],"prefix":"10.1371","volume":"18","author":[{"given":"Xiang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7852-1530","authenticated-orcid":true,"given":"Huitao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2445-6750","authenticated-orcid":true,"given":"Jie","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4183-0943","authenticated-orcid":true,"given":"Kelin","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"pcbi.1009943.ref001","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4020-9783-6","volume-title":"Recent advances in QSAR studies: methods and applications","author":"T Puzyn","year":"2010"},{"issue":"8","key":"pcbi.1009943.ref002","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","article-title":"Machine learning in chemoinformatics and drug discovery","volume":"23","author":"YC Lo","year":"2018","journal-title":"Drug discovery today"},{"issue":"6","key":"pcbi.1009943.ref003","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"JL Durant","year":"2002","journal-title":"Journal of chemical information and computer sciences"},{"issue":"1","key":"pcbi.1009943.ref004","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/1758-2946-3-33","article-title":"Open Babel: An open chemical toolbox","volume":"3","author":"NM O\u2019Boyle","year":"2011","journal-title":"Journal of cheminformatics"},{"issue":"6","key":"pcbi.1009943.ref005","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1021\/ci00028a014","article-title":"Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information","volume":"35","author":"LH Hall","year":"1995","journal-title":"Journal of Chemical Information and Computer Sciences"},{"issue":"5","key":"pcbi.1009943.ref006","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"D Rogers","year":"2010","journal-title":"Journal of chemical information and modeling"},{"key":"pcbi.1009943.ref007","unstructured":"Landrum G. 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