Deep Learning to Therapeutically Target Unreported Complexes

dc.creatorFernandez, Ariel
dc.date.accessioned2021-02-05T19:29:50Z
dc.date.accessioned2022-10-15T09:23:15Z
dc.date.available2021-02-05T19:29:50Z
dc.date.available2022-10-15T09:23:15Z
dc.date.created2021-02-05T19:29:50Z
dc.date.issued2019-05-21
dc.identifierFernandez, Ariel; Deep Learning to Therapeutically Target Unreported Complexes; Elsevier Science London; Trends In Pharmacological Sciences; 40; 8; 21-5-2019; 551-554
dc.identifier0165-6147
dc.identifierhttp://hdl.handle.net/11336/125003
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4370021
dc.description.abstractThe disruption of large protein–protein (PP) interfaces remains a challenge in targeted therapy. Designing drugs that compete with binding partners is daunting, especially when the structure of the protein complex is unknown. To address the problem we propose a deep protein databank (PDB) learning platform to discover targetable epitopes for complex-disruptive leads.
dc.languageeng
dc.publisherElsevier Science London
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.tips.2019.04.009
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(19)30086-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0165614719300860%3Fshowall%3Dtrue
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectDEEP LEARNING
dc.subjectPROTEIN COMPLEX
dc.subjectPROTEIN COMPLEX DISRUPTION
dc.subjectPROTEIN DATA BANK
dc.subjectTARGETED MOLECULAR THERAPY
dc.subjectUNKNOWN PROTEIN STRUCTURE
dc.titleDeep Learning to Therapeutically Target Unreported Complexes
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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