期刊
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
卷 12, 期 4, 页码 332-343出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/138620709788167980
关键词
Machine learning; virtual screening; data mining; drug discovery
资金
- EPSRC [GR/S75765/01]
- Engineering and Physical Sciences Research Council [GR/S75765/01] Funding Source: researchfish
In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naive Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.
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