title: In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  The accurate prediction model and the chemical toxicophores can provide some guidance for designing drugs with low toxicity.
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005. China.
authors.forename: Jing
authors.initials: J
authors.lastname: Lu
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005. China.
authors.forename: Pin
authors.initials: P
authors.lastname: Zhang
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005. China.
authors.forename: Xiao-Wen
authors.initials: XW
authors.lastname: Zou
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005. China.
authors.forename: Xiao-Qiang
authors.initials: XQ
authors.lastname: Zhao
authors.affiliation: Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Guangxi Normal University), Ministry of Education of China, Guilin 541004. China.
authors.forename: Ke-Guang
authors.initials: KG
authors.lastname: Cheng
authors.affiliation: State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, MOE-LSB and MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240. China.
authors.forename: Yi-Lei
authors.initials: YL
authors.lastname: Zhao
authors.affiliation: School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, 264005. China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Bi
authors.affiliation: State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203. China.
authors.forename: Ming-Yue
authors.initials: MY
authors.lastname: Zheng
authors.affiliation: State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203. China.
authors.forename: Xiao-Min
authors.initials: XM
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D000818:Animals; D002273:Carcinogens / Q000737:chemistry / Q000633:toxicity*; D003198:Computer Simulation; D062313:Databases, Pharmaceutical; D055808:Drug Discovery / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000209:etiology; D005123:Eye / Q000187:drug effects; D006801:Humans; D000069550:Machine Learning; D008954:Models, Biological; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity*; D004364:Pharmaceutical Preparations / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D012098:Reproduction / Q000187:drug effects; D012867:Skin / Q000187:drug effects; D018675:Toxicity Tests
chemical_list: D002273:Carcinogens; D009153:Mutagens; D004364:Pharmaceutical Preparations
keywords: ECFP_4; Laplacian-modified Bayesian; Local lazy learning; Toxicity profile