canSAR: update to the cancer translational research and drug discovery knowledgebase

Patrizio Di Micco; Albert A. Antolin; Costas Mitsopoulos; Eloy Villasclaras-Fernandez; Domenico Sanfelice; Daniela Dolciami; Pradeep Ramagiri; Ioan L. Mica; Joseph E. Tym; Philip W. Gingrich; Huabin Hu; Paul Workman; Bissan Al-Lazikani

doi:10.1093/nar/gkac1004

canSAR: update to the cancer translational research and drug discovery knowledgebase

Patrizio Di Micco, Albert A. Antolin, Costas Mitsopoulos, Eloy Villasclaras-Fernandez, Domenico Sanfelice, Daniela Dolciami, Pradeep Ramagiri, Ioan L. Mica, Joseph E. Tym, Philip W. Gingrich, Huabin Hu, Paul Workman, Bissan Al-Lazikani

Genomic Medicine

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

Original language	English (US)
Pages (from-to)	D1212-D1219
Journal	Nucleic acids research
Volume	51
Issue number	D1
DOIs	https://doi.org/10.1093/nar/gkac1004
State	Published - Jan 6 2023

ASJC Scopus subject areas

Genetics

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Di Micco, P., Antolin, A. A., Mitsopoulos, C., Villasclaras-Fernandez, E., Sanfelice, D., Dolciami, D., Ramagiri, P., Mica, I. L., Tym, J. E., Gingrich, P. W., Hu, H., Workman, P., & Al-Lazikani, B. (2023). canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic acids research, 51(D1), D1212-D1219. https://doi.org/10.1093/nar/gkac1004

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abstract = "canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.",

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AU - Antolin, Albert A.

AU - Mitsopoulos, Costas

AU - Villasclaras-Fernandez, Eloy

AU - Sanfelice, Domenico

AU - Dolciami, Daniela

AU - Ramagiri, Pradeep

AU - Mica, Ioan L.

AU - Tym, Joseph E.

AU - Gingrich, Philip W.

AU - Hu, Huabin

AU - Workman, Paul

AU - Al-Lazikani, Bissan

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canSAR: update to the cancer translational research and drug discovery knowledgebase

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