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dc.contributor.authorOrhobor, Oghenejokpeme I
dc.contributor.authorRehim, Abbi Abdel
dc.contributor.authorLou, Hang
dc.contributor.authorNi, Hao
dc.contributor.authorKing, Ross
dc.date.accessioned2022-06-17T01:19:55Z
dc.date.available2022-06-17T01:19:55Z
dc.date.issued2022-05
dc.identifier.issn2054-5703
dc.identifier.other35573039
dc.identifier.otherPMC9066299
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338185
dc.description.abstractThe representation of the protein-ligand complexes used in building machine learning models play an important role in the accuracy of binding affinity prediction. The Extended Connectivity Interaction Features (ECIF) is one such representation. We report that (i) including the discretized distances between protein-ligand atom pairs in the ECIF scheme improves predictive accuracy, and (ii) in an evaluation using gradient boosted trees, we found that the resampling method used in selecting the best hyperparameters has a strong effect on predictive performance, especially for benchmarking purposes.
dc.languageeng
dc.publisherThe Royal Society
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2054-5703
dc.sourcenlmid: 101647528
dc.subjectMachine Learning
dc.subjectScoring Functions
dc.subjectProtein Binding Affinity Prediction
dc.titleA simple spatial extension to the extended connectivity interaction features for binding affinity prediction.
dc.typeArticle
dc.date.updated2022-06-17T01:19:54Z
prism.issueIdentifier5
prism.publicationNameR Soc Open Sci
prism.volume9
dc.identifier.doi10.17863/CAM.85596
dcterms.dateAccepted2022-04-13
rioxxterms.versionofrecord10.1098/rsos.211745
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidOrhobor, Oghenejokpeme I [0000-0003-1178-611X]
dc.contributor.orcidKing, Ross [0000-0001-7208-4387]
dc.identifier.eissn2054-5703
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R022925/1)
cam.issuedOnline2022-05-04


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