{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:54:24Z","timestamp":1781211264459,"version":"3.54.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T00:00:00Z","timestamp":1632182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Medical Research Council","award":["EP\/L016044\/1"],"award-info":[{"award-number":["EP\/L016044\/1"]}]},{"DOI":"10.13039\/100004325","name":"AstraZeneca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time- and cost-intensive and heavily reliant on in vivo and in vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody\u2013antigen binding for antigens with no known antibody binders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We demonstrate that DLAB can be used both to improve antibody\u2013antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody\u2013antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The DLAB source code and pre-trained models are available at https:\/\/github.com\/oxpig\/dlab-public.<\/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\/btab660","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T23:14:50Z","timestamp":1631747690000},"page":"377-383","source":"Crossref","is-referenced-by-count":92,"title":["DLAB: deep learning methods for structure-based virtual screening of antibodies"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2579-0307","authenticated-orcid":false,"given":"Constantin","family":"Schneider","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford, OX1 3LB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Buchanan","sequence":"additional","affiliation":[{"name":"Antibody Discovery & Protein Engineering, R&D, AstraZeneca , Cambridge, CB2 0AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruck","family":"Taddese","sequence":"additional","affiliation":[{"name":"Discovery Sciences, R&D, AstraZeneca , Cambridge, CB2 0AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-2252","authenticated-orcid":false,"given":"Charlotte M","family":"Deane","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Oxford , Oxford, OX1 3LB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"2023020108441072000_btab660-B1","doi-asserted-by":"crossref","first-page":"e1006112","DOI":"10.1371\/journal.pcbi.1006112","article-title":"RosettaAntibodyDesign (RAbD): a general framework for computational antibody design","volume":"14","author":"Adolf-Bryfogle","year":"2018","journal-title":"PLoS Comput. 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