Author: 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
Title: canSAR: update to the cancer translational research and drug discovery knowledgebase Cord-id: sszzu7vo Document date: 2020_11_21
ID: sszzu7vo
Snippet: 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 in
Document: 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: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.
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