Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?

doi:10.1021/ci100343j

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	Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?
	Shen, Qiancheng 1; Xiong, Bing1 ; Zheng, Mingyue1 ; Luo, Xiaomin1 ; Luo, Cheng1 ; Liu, Xian 1; Du, Yun 1; Li, Jing1 ; Zhu, Weiliang1 ; Shen, Jingkang
刊名	JOURNAL OF CHEMICAL INFORMATION AND MODELING
	2011-02
卷号	51 期号:2 页码:386-397
ISSN号	1549-9596
DOI	10.1021/ci100343j
文献子类	Article
英文摘要	Fast and accurate predicting of the binding affinities of large sets of diverse protein ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K-i/K-d units from measured inhibition affinities and the highest Pearson's correlation coefficient of R-p(2) 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge based scoring functions in binding affinity prediction.
资助项目	State Key Program of Basic Research of China[2009CB918502] ; Hi-Tech Research and Development program of China[2006AA020404] ; National Natural Science Foundation of China[81001399]
WOS关键词	PROTEIN-LIGAND INTERACTIONS ; ENERGY FUNCTION ; DOCKING ; RECOGNITION ; PREDICTION ; COMPLEXES ; ACCURACY ; DATABASE ; SET ; DNA
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种	英语
出版者	AMER CHEMICAL SOC
WOS记录号	WOS:000287685700019
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/278616]
专题	药物发现与设计中心中科院受体结构与功能重点实验室新药研究国家重点实验室
通讯作者	Zheng, Mingyue
作者单位	1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China; 2.E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China
推荐引用方式 GB/T 7714	Shen, Qiancheng,Xiong, Bing,Zheng, Mingyue,et al. Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2011,51(2):386-397.
APA	Shen, Qiancheng.,Xiong, Bing.,Zheng, Mingyue.,Luo, Xiaomin.,Luo, Cheng.,...&Jiang, Hualiang.(2011).Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?.JOURNAL OF CHEMICAL INFORMATION AND MODELING,51(2),386-397.
MLA	Shen, Qiancheng,et al."Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?".JOURNAL OF CHEMICAL INFORMATION AND MODELING 51.2(2011):386-397.