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dc.contributor.authordi Micco, P
dc.contributor.authorAntolin, AA
dc.contributor.authorMitsopoulos, C
dc.contributor.authorVillasclaras-Fernandez, E
dc.contributor.authorSanfelice, D
dc.contributor.authorDolciami, D
dc.contributor.authorRamagiri, P
dc.contributor.authorMica, IL
dc.contributor.authorTym, JE
dc.contributor.authorGingrich, PW
dc.contributor.authorHu, H
dc.contributor.authorWorkman, P
dc.contributor.authorAl-Lazikani, B
dc.coverage.spatialEngland
dc.date.accessioned2023-02-21T13:30:23Z
dc.date.available2023-02-21T13:30:23Z
dc.date.issued2023-01-06
dc.identifier6851107
dc.identifier.citationNucleic Acids Research, 2023, 51 (D1), pp. D1212 - D1219en_US
dc.identifier.issn0305-1048
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5694
dc.identifier.eissn1362-4962
dc.identifier.eissn1362-4962
dc.identifier.doi10.1093/nar/gkac1004
dc.description.abstractcanSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.
dc.formatPrint
dc.format.extentD1212 - D1219
dc.languageeng
dc.language.isoengen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.relation.ispartofNucleic Acids Research
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectHumans
dc.subjectAlgorithms
dc.subjectDrug Discovery
dc.subjectKnowledge Bases
dc.subjectNeoplasms
dc.subjectTranslational Research, Biomedical
dc.subjectAntineoplastic Agents
dc.titlecanSAR: update to the cancer translational research and drug discovery knowledgebase.en_US
dc.typeJournal Article
dcterms.dateAccepted2022-11-28
dc.date.updated2023-02-21T13:29:42Z
rioxxterms.versionVoRen_US
rioxxterms.versionofrecord10.1093/nar/gkac1004en_US
rioxxterms.licenseref.startdate2023-01-06
rioxxterms.typeJournal Article/Reviewen_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/36624665
pubs.issueD1
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Computational Biology and Chemogenomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Signal Transduction & Molecular Pharmacology
pubs.organisational-group/ICR/ImmNet
pubs.publication-statusPublished
pubs.publisher-urlhttp://dx.doi.org/10.1093/nar/gkac1004
pubs.volume51
icr.researchteamComputational Biologyen_US
icr.researchteamSignal Trans & Mol Pharmaen_US
dc.contributor.icrauthorMitsopoulos, Konstantinos
dc.contributor.icrauthorWorkman, Paul
dc.contributor.icrauthorAl-Lazikani, Bissan
icr.provenanceDeposited by Mr Arek Surman on 2023-02-21. Deposit type is initial. No. of files: 1. Files: canSAR update to the cancer translational research and drug discovery knowledgebase.pdf


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