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.
Keywords:
Chemical probes; Chemical proteomics; Drug discovery; Machine learning; Target identification.
Copyright © 2019 Elsevier Ltd. All rights reserved.
Publication types
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Research Support, Non-U.S. Gov't
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Review
MeSH terms
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Antineoplastic Agents / chemistry
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Antineoplastic Agents / pharmacology
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Computer Simulation
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Drug Evaluation, Preclinical / methods*
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Humans
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Lipoxygenase / metabolism
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Machine Learning*
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Molecular Targeted Therapy
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Naphthoquinones / chemistry
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Naphthoquinones / pharmacology
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Pentacyclic Triterpenes / chemistry
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Pentacyclic Triterpenes / pharmacology
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Proteomics / methods*
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Receptors, Cannabinoid / metabolism
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Sesquiterpenes / chemistry
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Sesquiterpenes / pharmacology
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Sesquiterpenes, Guaiane / chemistry
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Sesquiterpenes, Guaiane / pharmacology
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Transient Receptor Potential Channels / metabolism
Substances
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Antineoplastic Agents
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Naphthoquinones
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Pentacyclic Triterpenes
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Receptors, Cannabinoid
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Sesquiterpenes
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Sesquiterpenes, Guaiane
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Transient Receptor Potential Channels
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englerin A
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beta-lapachone
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Lipoxygenase
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celastrol