Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8214
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dc.contributor.authorCichonska, Anna-
dc.contributor.authorRavikumar, Balaguru-
dc.contributor.authorAllaway, Robert J.-
dc.contributor.authorWan, Fangping-
dc.contributor.authorPark, Sungjoon-
dc.contributor.authorIsayev, Olexandr-
dc.contributor.authorLi, Shuya-
dc.date.accessioned2022-01-15T13:00:38Z-
dc.date.available2022-01-15T13:00:38Z-
dc.date.issued2021-
dc.identifier.issn2041-1723-
dc.identifier.urihttps://doi.org/10.1038/s41467-021-23165-1-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8214-
dc.description.abstractDespite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.en_US
dc.description.sponsorshipAcademy of FinlandAcademy of FinlandEuropean Commission [310507, 313267, 326238]; Cancer Research UKCancer Research UK; Brain Tumour Charity [REF: C42454/A28596]; Helse SOr-Ost [2020026]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [1U24DK116204-01, U54OD020353, U24CA224370, U24TR002278, U01CA239108]; AbbVieAbbVie; Bayer Pharma AG; Boehringer IngelheimBoehringer Ingelheim; Canada Foundation for InnovationCanada Foundation for InnovationCGIAR; Eshelman Institute for Innovation; Genome CanadaGenome Canada; Innovative Medicines Initiative [ULTRA-DD 115766]; Wellcome TrustWellcome TrustEuropean Commission; JanssenJohnson & JohnsonJohnson & Johnson USAJanssen Biotech Inc; Merck Kga; Merck Sharp DohmeMerck & Company; Novartis Pharma AG; Ontario Ministry of Economic Development and Innovation; PfizerPfizer; Sao Paulo Research Foundation-FAPESPFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP); TakedaTakeda Pharmaceutical Company Ltd; National Science Foundation (NSF)National Science Foundation (NSF) [CHE-1802789, CHE-2041108]; Eshelman Institute for Innovation (EII) awards; Molecular Sciences Software Institute (MolSSI) Software Fellowship; NVIDIA Graduate Fellowshipen_US
dc.description.sponsorshipThe authors thank the IDG Kinase Data and Resource Generation Center for generating new sets of target activity data for the Challenge Rounds 1 and 2, Olle Hansson (FIMM) for technical assistance with DrugTargetCommons platform, Tianduanyi Wang (FIMM) for his help with the baseline submissions, Anna Goldenberg (University of Toronto, Canada) and Chloe-Agathe Azencott (Institut Curie, France) for organizing the DREAM Idea Challenge, and Barbara Rieck and Ladan Naghavian for the bioactivity profiling at DiscoverX (Eurofins Corporation). T.A. acknowledges support from the Academy of Finland (grants 310507, 313267, 326238), Cancer Research UK and the Brain Tumour Charity (grant REF: C42454/A28596), and Helse SOr-Ost (grant No. 2020026). C.W., T.W., D.D. acknowledge support from the National Institutes of Health (1U24DK116204-01). The SGC is a registered charity that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada, Innovative Medicines Initiative (ULTRA-DD 115766), Wellcome Trust, Janssen, Merck Kga, Merck Sharp & Dohme, Novartis Pharma AG, Ontario Ministry of Economic Development and Innovation, Pfizer, Sao Paulo Research Foundation-FAPESP, and Takeda. O.I. acknowledges support from the National Science Foundation (NSF CHE-1802789 and CHE-2041108), and Eshelman Institute for Innovation (EII) awards. O.I. thanks the OpenEye Free Academic Licensing Program for providing a free academic license for their chemistry toolkit. M.P. acknowledges support from The Molecular Sciences Software Institute (MolSSI) Software Fellowship and NVIDIA Graduate Fellowship. We gratefully acknowledge the support and hardware donation from NVIDIA Corporation. J.G. acknowledges support from the National Institutes of Health (U54OD020353). T.I.O. acknowledges support from the National Institutes of Health (U24CA224370; U24TR002278; U01CA239108).en_US
dc.language.isoenen_US
dc.publisherNature Portfolioen_US
dc.relation.ispartofNature Communicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDrugen_US
dc.subjectPharmacologyen_US
dc.subjectPredictionen_US
dc.subjectDiscoveryen_US
dc.subjectPackageen_US
dc.titleCrowdsourced Mapping of Unexplored Target Space of Kinase Inhibitorsen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.volume12en_US
dc.identifier.issue1en_US
dc.authoridAittokallio, Tero / 0000-0002-0886-9769-
dc.identifier.wosWOS:000661571900005en_US
dc.institutionauthorTan, Mehmet-
dc.identifier.pmid34083538en_US
dc.identifier.doi10.1038/s41467-021-23165-1-
dc.authorwosidAittokallio, Tero / B-6583-2009-
dc.authorwosidLucic, Bono / J-3813-2012-
dc.authorwosidHwang, Ming-jing / E-9210-2012-
dc.authorwosidAllaway, Robert / ABE-1264-2021-
dc.authorwosidDogan, Tunca / B-5274-2017-
dc.authorwosidLucic, Bono / L-7472-2013-
dc.authorwosidAtalay, Volkan / M-2256-2016-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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