Belew Richard K, Forli Stefano, Goodsell David S, O'Donnell T J, Olson Arthur J
Cognitive Science, University of California San Diego , La Jolla, California 92093, United States.
Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.
J Chem Inf Model. 2016 Aug 22;56(8):1597-607. doi: 10.1021/acs.jcim.6b00248. Epub 2016 Jul 25.
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.
我们介绍了ADChemCast，这是一种利用虚拟筛选结果来创建目标结合位点更丰富表示的方法，可用于改进化合物的排名并表征配体-受体特异性的决定因素。ADChemCast基于化学相似结构片段内共享的成对受体-配体相互作用对配体的对接构象进行聚类，构建一组结合剂和非结合剂特有的属性。然后使用机器学习从信息最丰富的属性中构建规则，用于重新排列化合物的排名。在本报告中，我们使用ADChemCast来改进来自增强型有用诱饵数据库（DUD-E）的11种不同蛋白质中化合物的排名，并证明该方法在表征HIV逆转录酶相关结合属性方面的实用性。
