期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 57, 期 8, 页码 1793-1806出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.7b00017
关键词
-
类别
资金
- National Natural Science Foundation of China [21003048, 21433004]
- National Key Research and Development Plan [2016YFA0501700]
- Shanghai Natural Science Foundation [14ZR1411900]
- Shanghai Putuo District [2014-A-02]
- Fundamental Research Funds for the Central Universities
- State Key Laboratory of Precision Spectroscopy, East China Normal University
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai
A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector :machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives, Experimentally, derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据