Lu J, Zhang P, Zou XW, Zhao XQ, Cheng KG, Zhao YL, Bi Y, Zheng MY, Luo XM. In Silico Prediction of Chemical Toxicity Profile Using Local Lazy Learning.
Comb Chem High Throughput Screen 2017;
20:346-353. [PMID:
28215144 DOI:
10.2174/1386207320666170217151826]
[Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 01/19/2017] [Accepted: 02/07/2017] [Indexed: 11/22/2022]
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.
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