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- W2989850839 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." @default.
- W2989850839 created "2019-12-05" @default.
- W2989850839 creator A5003360817 @default.
- W2989850839 creator A5082017604 @default.
- W2989850839 date "2020-06-01" @default.
- W2989850839 modified "2023-09-30" @default.
- W2989850839 title "Machine learning for target discovery in drug development" @default.
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- W2989850839 doi "https://doi.org/10.1016/j.cbpa.2019.10.003" @default.
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