Journal article

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

Raghad Al-Jarf, Alex GC de Sa, Douglas E Pires, David B Ascher

JOURNAL OF CHEMICAL INFORMATION AND MODELING | AMER CHEMICAL SOC | Published : 2021

Abstract

The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure-activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be act..

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Grants

Awarded by Medical Research Council


Awarded by National Health and Medical Research Council of Australia


Awarded by Wellcome Trust


Awarded by Jack Brockhoff Foundation


Funding Acknowledgements

R.A. is funded with a PhD scholarship from the Kingdom of Saudi Arabia. A.G.C.S. acknowledges the Joe White Bequest Fellowship for its support. This work was supported in part by the Medical Research Council (MR/M026302/1 to D.B.A. and D.E.V.P.); the National Health and Medical Research Council of Australia (GNT1174405 to D.B.A.), the Wellcome Trust (093167/Z/10/Z), Jack Brockhoff Foundation (JBF 4186, 2016 to D.B.A.), and the Victorian Government's Operational Infrastructure Support Program. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.