Machine learning for target discovery in drug development

• Published in: Current opinion in chemical biology Vol. 56; pp. 16 - 22
• Main Authors: Rodrigues, Tiago, Bernardes, Gonçalo J.L.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2020; Elsevier
• Subjects: Antineoplastic Agents - chemistry; Antineoplastic Agents - pharmacology; Chemical probes; Chemical proteomics; Computer Simulation; Drug discovery; Drug Evaluation, Preclinical - methods; Humans; Lipoxygenase - metabolism; Machine Learning; Molecular Targeted Therapy; Naphthoquinones - chemistry; Naphthoquinones - pharmacology; Pentacyclic Triterpenes - chemistry; Pentacyclic Triterpenes - pharmacology; Proteomics - methods; Receptors, Cannabinoid - metabolism; Sesquiterpenes - chemistry; Sesquiterpenes - pharmacology; Sesquiterpenes, Guaiane - chemistry; Sesquiterpenes, Guaiane - pharmacology; Target identification; Transient Receptor Potential Channels - metabolism; Drug discovery; Chemical probes; Target identification; Chemical proteomics; Machine learning
• ISSN: 1367-5931; 1879-0402; 1879-0402
• DOI: 10.1016/j.cbpa.2019.10.003

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.