In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning

Title:In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning

Volume: 20 Issue: 4

Author(s): Jing Lu*, Pin Zhang, Xiao-Wen Zou, Xiao-Qiang Zhao, Ke-Guang Cheng, Yi-Lei Zhao, Yi Bi, Ming-Yue Zheng*Xiao-Min Luo*

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

• State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203,China

• State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203,China

Keywords: Toxicity profile, Local lazy learning, ECFP_4, Laplacian-modified Bayesian.

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.

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.

Cite this article as:

Lu Jing*, Zhang Pin, Zou Xiao-Wen, Zhao Xiao-Qiang, Cheng Ke-Guang, Zhao Yi-Lei, Bi Yi, Zheng Ming-Yue*, Luo Xiao-Min*, In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning, Combinatorial Chemistry & High Throughput Screening 2017; 20 (4) . https://dx.doi.org/10.2174/1386207320666170217151826

DOI https://dx.doi.org/10.2174/1386207320666170217151826	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402