CanSAR: update to the cancer translational research and drug discovery knowledgebase. Issue Volume 49:Issue D1(2021) (21st November 2020)

• Record Type: Journal Article
• Authors: Mitsopoulos, Costas; Di Micco, Patrizio; Fernandez, Eloy Villasclaras; Dolciami, Daniela; Holt, Esty; Mica, Ioan L; Coker, Elizabeth A; Tym, Joseph E; Campbell, James; Che, Ka Hing; Ozer, Bugra; Kannas, Christos; Antolin, Albert A; Workman, Paul; Al-Lazikani, Bissan
• Is Part Of: Nucleic acids research. Volume 49:Issue D1(2021)
• Page Start: D1074
• Page End: D1082
• Publication Date: 2020-11-21
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkaa1059
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: canSAR (http://cansar.icr.ac.uk ) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSAR black . We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.