{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:02:32Z","timestamp":1783386152004,"version":"3.54.6"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T00:00:00Z","timestamp":1530835200000},"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\/501100003329","name":"MINECO","doi-asserted-by":"publisher","award":["BIO2017-82628-P"],"award-info":[{"award-number":["BIO2017-82628-P"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002924","name":"FEDER","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002924","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union\u2019s Horizon 2020 Research","award":["675451"],"award-info":[{"award-number":["675451"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fields in target protein pockets. We use an end-to-end deep learning framework trained on experimental protein\u2013ligand complexes with the intention of mimicking a chemist\u2019s intuition at manually placing atoms when designing a new compound. We show that these models can generate spatial images of ligand chemical properties like occupancy, aromaticity and donor\u2013acceptor matching the protein pocket.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The predicted fields considerably overlap with those of unseen ligands bound to the target pocket. Maximization of the overlap between the predicted fields and a given ligand on the Astex diverse set recovers the original ligand crystal poses in 70 out of 85 cases within a threshold of 2\u2009\u00c5 RMSD. We expect that these models can be used for guiding structure-based drug discovery approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>LigVoxel is available as part of the PlayMolecule.org molecular web application suite.<\/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\/bty583","type":"journal-article","created":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T19:39:34Z","timestamp":1530733174000},"page":"243-250","source":"Crossref","is-referenced-by-count":60,"title":["LigVoxel: inpainting binding pockets using 3D-convolutional neural networks"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4143-4609","authenticated-orcid":false,"given":"Miha","family":"Skalic","sequence":"first","affiliation":[{"name":"Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Varela-Rial","sequence":"additional","affiliation":[{"name":"Acellera, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard","family":"Mart\u00ednez-Rosell","sequence":"additional","affiliation":[{"name":"Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gianni","family":"De Fabritiis","sequence":"additional","affiliation":[{"name":"Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)"},{"name":"Instituci\u00f3 Catalana de Recerca i Estudis Avan\u00e7ats (ICREA), Passeig Lluis Companys 23, Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"key":"2023013107232503800_bty583-B1","doi-asserted-by":"crossref","DOI":"10.15252\/msb.20156651","article-title":"Deep learning for computational biology","volume":"12","author":"Angermueller","year":"2016","journal-title":"Mol. 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