{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:30:23Z","timestamp":1774369823000,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100013209","name":"Hellenic Foundation for Research and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013209","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003448","name":"General Secretariat for Research and Technology","doi-asserted-by":"publisher","award":["122"],"award-info":[{"award-number":["122"]}],"id":[{"id":"10.13039\/501100003448","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The knowledge of potentially druggable binding sites on proteins is an important preliminary step toward the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3D voxelized grids are placed on the protein\u2019s surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of the method along with trained models are freely available at https:\/\/github.com\/stemylonas\/DeepSurf.git.<\/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\/btab009","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T22:31:42Z","timestamp":1609885902000},"page":"1681-1690","source":"Crossref","is-referenced-by-count":106,"title":["DeepSurf: a surface-based deep learning approach for the prediction of ligand binding sites on proteins"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-3618","authenticated-orcid":false,"given":"Stelios K","family":"Mylonas","sequence":"first","affiliation":[{"name":"Information Technologies Institute, Centre for Research and Technology Hellas , Thessaloniki 57001, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apostolos","family":"Axenopoulos","sequence":"additional","affiliation":[{"name":"Information Technologies Institute, Centre for Research and Technology Hellas , Thessaloniki 57001, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3814-6710","authenticated-orcid":false,"given":"Petros","family":"Daras","sequence":"additional","affiliation":[{"name":"Information Technologies Institute, Centre for Research and Technology Hellas , Thessaloniki 57001, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"2023051709555043300_btab009-B1","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/TCBB.2015.2498553","article-title":"Similarity search of flexible 3d molecules combining local and global shape descriptors","volume":"13","author":"Axenopoulos","year":"2016","journal-title":"IEEE\/ACM Trans. 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