About us

Our History and Goals

canSAR is public resource, powered by an international collaboration led from University of Texas MD Anderson Cancer Center, in collaboration with the Computational Biology and Chemogenomics team in the CRUK and The Institute of Cancer Research , London UK. Its goal is to help inform cancer drug discovery and translational research worldwide.

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

canSAR aims 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.ai works on latest versions of Firefox, Chrome, Safari and Microsoft Edge.

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

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

Funding: Cansar.ai is provided through the generous support of:

canSAR.ai was built thanks to support from Cancer Research UK Drug Discovery Committee strategic award ‘canSAR: enhancing the drug discovery knowledgebase’ (C35696/A23187, 2016-2022)

We are also grateful to the Wellcome Trust Biomedical Resource Award to the Chemical Probes Portal (WT212969/Z/18/Z)

Chemical Standardization Software: Chemaxon logo https://www.knime.com/ logo RdKit logo

Meet the Team

Cite canSAR

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