Thursday, February 14, 2013
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Computational approach for
prioritizing potential cancer targets
computational approach that predicts the druggability of cancer-associated
proteins could help prioritize targets in small molecule discovery programs.
The computational approach integrates target class, bioactivity data, protein
structural information and homology modeling to estimate a protein's
druggability. From a list of 479 genes known to be altered in cancer, the
method identified 29 oncogenes and 16 tumor suppressors predicted to be
druggable but for which few or no small molecule ligands had yet been
reported. Next steps include incorporating additional cancer genomic data and
could include prioritizing additional types of gene lists such as those from
synthetic lethal screens in cancer cell lines.
The computational method is freely available through the canSAR
database hosted by The Institute of Cancer Research
(see Cancer target selection pressure, page 1).
Published online Feb. 14, 2013
status not applicable
Patel, M.N. et al. Nat.
Rev. Drug Discov.; published online
Dec. 31, 2012;
Contact: Bissan Al-Lazikani,
The Institute of Cancer Research, London, U.K.
Contact: Paul Workman, same affiliation as above
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