In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning

doi:10.2174/1386207320666170217151826

CORC > 上海药物研究所 > 中国科学院上海药物研究所 > 药物发现与设计中心

	In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning
	Lu, Jing 1; Zhang, Pin 1; Zou, Xiao-Wen 1; Zhao, Xiao-Qiang 1; Cheng, Ke-Guang 2; Zhao, Yi-Lei 3,4; Bi, Yi 1,5; Zheng, Ming-Yue6 ; Luo, Xiao-Min6
刊名	COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
	2017
卷号	20 期号:4 页码:346-353
关键词	Toxicity profile Local lazy learning ECFP_4 Laplacian-modified Bayesian
ISSN号	1386-2073
DOI	10.2174/1386207320666170217151826
文献子类	Article
英文摘要	Background: Chemical toxicity is an important reason for late-stage failure in drug R&D. However, it is time-consuming and expensive to identify the multiple toxicities of compounds using the traditional experiments. Thus, it is attractive to build an accurate prediction model for the toxicity profile of compounds. Materials and Methods: In this study, we carried out a research on six types of toxicities: (I) Acute Toxicity; (II) Mutagenicity; (III) Tumorigenicity; (IV) Skin and Eye Irritation; (V) Reproductive Effects; (VI) Multiple Dose Effects, using local lazy learning (LLL) method for multi-label learning. 17,120 compounds were split into the training set and the test set as a ratio of 4:1 by using the Kennard-Stone algorithm. Four types of properties, including molecular fingerprints (ECFP_4 and FCFP_4), descriptors, and chemical-chemical-interactions, were adopted for model building. Results: The model 'ECFP_4+LLL' yielded the best performance for the test set, while balanced accuracy (BACC) reached 0.692, 0.691, 0.666, 0.680, 0.631, 0.599 for six types of toxicities, respectively. Furthermore, some essential toxicophores for six types of toxicities were identified by using the Laplacian-modified Bayesian model. Conclusion: The accurate prediction model and the chemical toxicophores can provide some guidance for designing drugs with low toxicity.
资助项目	National Natural Science Foundation of China[81603024] ; National Key Research & Development Plan[2016YF1201003] ; National Basic Research Program[2015CB910304] ; Hi-Tech Research and Development Program of China[2014AA01A302] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA12050201] ; Colleges and universities in Shandong Province science and technology projects[J16LM02] ; State Key Laboratory of Drug Research[SIMM1705KF-07] ; Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Guangxi Normal University), Ministry of Education of China[CMEMR2015-B09] ; Ministry of Education Key Laboratory of Scientific and Engineering Computing[00000000]
WOS关键词	ADMET EVALUATION ; VECTOR MACHINE ; HERG ; PROTEINS ; ADDUCTS ; STITCH ; DNA
WOS研究方向	Biochemistry & Molecular Biology ; Chemistry ; Pharmacology & Pharmacy
语种	英语
出版者	BENTHAM SCIENCE PUBL LTD
WOS记录号	WOS:000408462300009
内容类型	期刊论文
源URL	[http://119.78.100.183/handle/2S10ELR8/275699]
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	Lu, Jing; Zheng, Ming-Yue; Luo, Xiao-Min
作者单位	1.Yantai Univ, Collaborat Innovat Ctr Adv Drug Delivery Syst & B, Key Lab Mol Pharmacol & Drug Evaluat, Sch Pharm,Minist Educ, Yantai 264005, Peoples R China; 2.Guangxi Normal Univ, Minist Educ China, Key Lab Chem & Mol Engn Med Resources, Guilin 541004, Peoples R China; 3.Shanghai Jiao Tong Univ, State Key Lab Microbial Metab, Joint Int Res Lab Metab & Dev Sci, MOE LSB, Shanghai 200240, Peoples R China; 4.Shanghai Jiao Tong Univ, Sch Life Sci & Biotechnol, MOE LSC, Shanghai 200240, Peoples R China; 5.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China; 6.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China
推荐引用方式 GB/T 7714	Lu, Jing,Zhang, Pin,Zou, Xiao-Wen,et al. In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning[J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,2017,20(4):346-353.
APA	Lu, Jing.,Zhang, Pin.,Zou, Xiao-Wen.,Zhao, Xiao-Qiang.,Cheng, Ke-Guang.,...&Luo, Xiao-Min.(2017).In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning.COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,20(4),346-353.
MLA	Lu, Jing,et al."In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning".COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING 20.4(2017):346-353.