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can SAR BLACK

( Version: 1.5.0 )

The integrated knowledge-base 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.

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

CONTACT US

canSAR is an academic project provided to the community through a CRUK Strategic Award.

We welcome your input, suggestions and bug reports. Please contact us at:

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