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Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.

Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.
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Belew RK, Forli S, Goodsell DS, O'Donnell TJ, Olson AJ,


Belew RK, Forli S, Goodsell DS, O'Donnell TJ, Olson AJ, (click to view)

Belew RK, Forli S, Goodsell DS, O'Donnell TJ, Olson AJ,

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Journal of chemical information and modeling 2016 07 2556(8) 1597-607 doi 10.1021/acs.jcim.6b00248

Abstract

We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and nonbinders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E) and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.

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