Machine learning for target discovery in drug development.
Machine learning for target discovery in drug development.
Curr Opin Chem Biol. 2019 Nov 14;56:16-22
Authors: Rodrigues T, Bernardes GJL
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.
PMID: 31734566 [PubMed - as supplied by publisher]
Source: Current Opinion in Chemical Biology - Category: Biochemistry Authors: Rodrigues T, Bernardes GJL Tags: Curr Opin Chem Biol Source Type: research
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