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dc.contributor.authorLee, Alphaen
dc.contributor.authorBrenner, MPen
dc.contributor.authorColwell, Lucyen
dc.date.accessioned2016-12-05T10:49:11Z
dc.date.available2016-12-05T10:49:11Z
dc.date.issued2016-11-29en
dc.identifier.issn0027-8424
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/261426
dc.description.abstractRapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.
dc.description.sponsorshipThis research was funded by a grant from Roche Pharmaceuticals. A.A.L. acknowledges the support of a Fulbright Fellowship. L.J.C. was supported by a Next Generation Fellowship, and a Marie Curie Career Integration Grant (Evo-Couplings, Grant 631609). M.P.B. is an investigator of the Simons Foundation, and also acknowledges support from the National Science Foundation through DMS-1411694.
dc.languageENGen
dc.language.isoenen
dc.publisherProceedings of the National Academy of Sciences of the United States of America
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectdrug discoveryen
dc.subjectrandom matrix theoryen
dc.subjectprotein–ligand affinityen
dc.subjectcomputational pharmacologyen
dc.subjectstatistical physicsen
dc.titlePredicting protein-ligand affinity with a random matrix frameworken
dc.typeArticle
prism.endingPage13569
prism.issueIdentifier48en
prism.publicationDate2016en
prism.publicationNameProceedings of the National Academy of Sciencesen
prism.startingPage13564
prism.volume113en
dc.identifier.doi10.17863/CAM.6614
dcterms.dateAccepted2016-09-29en
rioxxterms.versionofrecord10.1073/pnas.1611138113en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc/4.0/en
rioxxterms.licenseref.startdate2016-11-29en
dc.contributor.orcidColwell, Lucy [0000-0003-3148-0337]
dc.identifier.eissn1091-6490
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEuropean Commission (631609)
cam.issuedOnline2016-11-16en
rioxxterms.freetoread.startdate2017-05-16


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Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International