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Virtual Screening of Chinese Herbs with Random Forest

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journal contribution
posted on 26.03.2007, 00:00 by Thomas M. Ehrman, David J. Barlow, Peter J. Hylands
Random Forest, a form of multiple decision trees, has been used to screen a database of Chinese herbal constituents for potential inhibitors against several therapeutically important molecular targets. These comprise cyclic adenosine 3‘-5‘-monophosphate phosphodiesterases, protein kinase A, cyclooxygenases, lipoxygenases, aldose reductase, and three HIV targets-integrase, protease, and reverse transcriptase. In addition, compounds were identified which may inhibit the expression of inducible nitric oxide synthase and/or nitric oxide production in vivo. A total of 240 Chinese herbs containing 8264 compounds were screened in silico, including many used on a regular basis in traditional Chinese medicine. Active compounds were selected from another database of 2597 phytochemicals and related natural products with known target affinities and covered a wide range of structural classes. Random Forest was found to perform well, even on highly unbalanced data characteristic of ligand-based screening where the compounds to be screened are far more numerous than the number of active compounds used in training. Despite a conservative screening protocol, a wide variety of compounds from Chinese herbs were hit. Of particular interest were the relatively large number of herbs predicted to inhibit multiple targets, as well as a number which appeared to contain inhibitors of the same target from different phytochemical classes. The latter point to the possibility that individual species may make use of alternative phytochemical strategies in target inhibition. A literature search provided evidence to support 83 herb−target predictions.