文献类型：期刊文献

英文题名：Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs

作者：Wang, Xia[1];Pan, Chenxu[2];Gong, Jiayu[2];Liu, Xiaofeng[1];Li, Honglin[1,2]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：56

期号：6

起止页码：1175

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20162702561802);WOS:【SCI-EXPANDED(收录号:WOS:000378826800026)】；

基金：This work was supported by the National Natural Science Foundation of China (grants 81230090 and 81222046), the Shanghai Committee of Science and Technology (grant 14431902400), the National S&T Major Project of China (Grant 2013ZX09507004), and the Twelfth Five-Year National Science & Technology Support Program (Grant 2012BAI29B06). H.L. is also sponsored by the Innovation Program of Shanghai Municipal Education Commission (grant 13SG32) and Fok Ying Tung Education Foundation (141035).

语种：英文

外文关键词：Drug interactions - Probability distributions - Query processing - Forecasting

摘要：PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.