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Drug Discov Today. 2018 Aug;23(8):1538-1546. doi: 10.1016/j.drudis.2018.05.010. Epub 2018 May 8.

Machine learning in chemoinformatics and drug discovery.

Author information

1
Department of Bioengineering, Stanford University, Stanford, CA, USA.
2
Department of Bioengineering, Stanford University, Stanford, CA, USA. Electronic address: rbaltman@stanford.edu.

Abstract

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

PMID:
29750902
PMCID:
PMC6078794
[Available on 2019-08-01]
DOI:
10.1016/j.drudis.2018.05.010
[Indexed for MEDLINE]
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