Publications by authors named "Thomas M Ehrman"

Chinese herbs were screened for compounds which may be active against four targets involved in inflammation, using pharmacophore-assisted docking. Multiple LigandScout (LS) pharmacophores built from ligand-receptor complexes in the protein databank (PDB) were first employed to select compounds. These compounds were then docked using LS-derived templates and ranked according to docking score.

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Distribution patterns of 8411 compounds from 240 Chinese herbs were analyzed in relation to the herbal categories of traditional Chinese medicine (TCM), using Random Forest (RF) and self-organizing maps (SOM). RF was used first to construct TCM profiles of individual compounds, which describe their affinities for 28 major herbal categories, while simultaneously minimizing the level of noise associated with the complex array of diverse phytochemicals found in herbs from each category. Profiles were then reduced and visualized with SOM.

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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.

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Two databases have been constructed to facilitate applications of cheminformatics and molecular modeling to medicinal plants. The first contains data on known chemical constituents of 240 commonly used Chinese herbs, the other contains information on target specificities of bioactive plant compounds. Structures are available for all compounds.

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