Machine learning for target discovery in drug development.
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Rodrigues, Tiago
Bernardes, Gonçalo JL
Abstract
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.
Description
Keywords
Chemical probes, Chemical proteomics, Drug discovery, Machine learning, Target identification, Antineoplastic Agents, Computer Simulation, Drug Evaluation, Preclinical, Humans, Lipoxygenase, Machine Learning, Molecular Targeted Therapy, Naphthoquinones, Pentacyclic Triterpenes, Proteomics, Receptors, Cannabinoid, Sesquiterpenes, Sesquiterpenes, Guaiane, Transient Receptor Potential Channels
Journal Title
Curr Opin Chem Biol
Conference Name
Journal ISSN
1367-5931
1879-0402
1879-0402
Volume Title
56
Publisher
Elsevier BV
Publisher DOI
Sponsorship
Royal Society (URF\R\180019)
European Commission Horizon 2020 (H2020) Spreading Excellence and Widening Participation (807281)
European Commission Horizon 2020 (H2020) Spreading Excellence and Widening Participation (807281)
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).