Machine learning for target discovery in drug development.
Current opinion in chemical biology
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Rodrigues, T., & Lopes Bernardes, G. (2020). Machine learning for target discovery in drug development.. Current opinion in chemical biology, 56 16-22. https://doi.org/10.1016/j.cbpa.2019.10.003
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically-manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.
T.R. is an Investigador Auxiliar supported by FCT Portugal (CEECIND/00887/2017). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant 807281) and FCT/FEDER (02/SAICT/2017, Grant 28333) for funding. G.J.L.B. is a Royal Society University Research Fellow (URF\R\180019) and a FCT Investigator (IF/00624/2015).
Royal Society (URF\R\180019)
European Commission Horizon 2020 (H2020) Spreading Excellence and Widening Participation (807281)
External DOI: https://doi.org/10.1016/j.cbpa.2019.10.003
This record's URL: https://www.repository.cam.ac.uk/handle/1810/297727
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Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/