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
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.
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Data and stats: To view statistics of canSAR.ai data please refer to the
Funding: Cansar.ai is provided through the generous support of:
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: