About us

canSAR is developed by the canSAR Team | The Department of Data Science in collaboration with the Computational Biology and Chemogenomics team in the CRUK Cancer Therapeutics Unit at The Institute of Cancer Research , London UK.

canSAR is an integrated knowledgebase that brings together multidisciplinary data across biology, chemistry, pharmacology, structural biology, cellular networks and clinical annotations, and applies machine learning approaches to provide drug-discovery useful predictions.

canSAR’s goals: To enable translational research and drug discovery through providing this knowledge to researchers from across different disciplines. It provides a single information portal to answer complex multi-disciplinary questions and help hypothesis generation. It provides a single information portal to answer complex multi-disciplinary questions including - among many others: what is known about a protein, in which cancers is it expressed or mutated and what chemical tools and cell line models can be used to experimentally probe its activity? What is known about a drug, its cellular sensitivity profile and what proteins is it known to bind that may explain unusual bioactivity?

This is a free resource for the community. We invite collaborators to give feedback, input and contact us to ensure that we deliver what the translational research community needs.

Browser compatibility: canSAR works on latest versions of Firefox, Chrome, Safari and Microsoft Edge.

Terms of use: canSAR is freely available for all researchers. By using canSAR you are agreeing to these Terms of Use .

Data and stats: To view statistics of canSAR data please refer to the Data Sources .

Funding: canSAR is funded by the Cancer Research UK Drug Discovery Committee strategic award ‘canSAR: enhancing the drug discovery knowledgebase’ C35696/A23187.

We are also grateful to the Wellcome Trust Biomedical Resource Award to the Chemical Probes Portal (WT212969/Z/18/Z) and The Sir Henry Wellcome Postdoctoral Fellowship (WT 204735/Z/16/Z) for support. We thank the Heather Beckwith Charitable Settlement and The John L Beckwith Charitable Trusts for their generous support of our High Performance Computing facility.

Chemical Standardization Software:

Kindly cite canSAR:

canSAR: update to the cancer translational research and drug discovery knowledgebase , Costas Mitsopoulos, Patrizio Di Micco, Eloy Villasclaras Fernandez, Daniela Dolciami, Esty Holt, Ioan L. Mica, Elizabeth A. Coker, Joseph E. Tym, James Campbell, Ka Hing Che, Bugra Ozer, Christos Kannas, Albert A. Antolin, Paul Workman, Bissan Al-Lazikani, Nucl. Acids Res. 2021 Jan 8;49(D1):D1074-D1082. doi: 10.1093/nar/gkaa1059