Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/8214
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DC Field | Value | Language |
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dc.contributor.author | Cichońska, A. | - |
dc.contributor.author | Ravikumar, B. | - |
dc.contributor.author | Allaway, R.J. | - |
dc.contributor.author | Wan, F. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Isayev, O. | - |
dc.contributor.author | Aittokallio, T. | - |
dc.date.accessioned | 2022-01-15T13:00:38Z | - |
dc.date.available | 2022-01-15T13:00:38Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://doi.org/10.1038/s41467-021-23165-1 | - |
dc.description.abstract | Despite 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benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data.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. © 2021, The Author(s). | en_US |
dc.description.sponsorship | ; National Cancer Institute, NCI, (U01CA239108, U24CA224370); National Center for Advancing Translational Sciences, NCATS, (U24TR002278); National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK, (U24DK116204); European Commission, EC, (115766); National Science Foundation, NSF, (1802789); NIH Office of the Director, OD, (U54OD020353) | en_US |
dc.language.iso | en | en_US |
dc.publisher | Nature Research | en_US |
dc.relation.ispartof | Nature Communications | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.title | Crowdsourced Mapping of Unexplored Target Space of Kinase Inhibitors | en_US |
dc.type | Article | en_US |
dc.department | TOBB University of Economics and Technology | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 1 | en_US |
dc.authorid | Aittokallio, Tero / 0000-0002-0886-9769 | - |
dc.identifier.wos | WOS:000661571900005 | - |
dc.identifier.scopus | 2-s2.0-85107545818 | - |
dc.institutionauthor | Tan, Mehmet | - |
dc.identifier.pmid | 34083538 | - |
dc.identifier.doi | 10.1038/s41467-021-23165-1 | - |
dc.authorscopusid | 56922289600 | - |
dc.authorscopusid | 57194832786 | - |
dc.authorscopusid | 57189518250 | - |
dc.authorscopusid | 57205138684 | - |
dc.authorscopusid | 58161443100 | - |
dc.authorscopusid | 23060975100 | - |
dc.authorscopusid | 57209788542 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
dc.identifier.wosquality | Q1 | - |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.grantfulltext | none | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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