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Publicação
Machine learning for target discovery in drug development
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Orientador(es)
Resumo(s)
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.
Descrição
© 2019 Elsevier Ltd. All rights reserved.
Palavras-chave
Chemical probes Chemical proteomics Drug discovery Machine learning Target identification
Contexto Educativo
Citação
Current opinion in chemical biology, 56, 16-22
Editora
Elsevier