Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to Vibrio fischeri

Author:

Wu Feng1,Zhang Xinhua1,Fang Zhengjun1,Yu Xinliang1

Affiliation:

1. Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration, College of Materials and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104, China

Abstract

Vibrio fischeri is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class − 1 with log1/IBC50 ≤ 4.2 and Class + 1 with log1/IBC50 > 4.2, the unit of IBC50: mol/L) by utilizing a large data set of 601 toxicity log1/IBC50 of organic compounds to Vibrio fischeri. Dragon software was used to calculate 4885 molecular descriptors for each compound. Stepwise multiple linear regression (MLR) analysis was used to select the descriptor subset for the models. The ten molecular descriptors used in the classification model reflect the structural information on the Michael-type addition of nucleophiles, molecular branching, molecular size, polarizability, hydrophobic, and so on. Furthermore, these descriptors were interpreted from the point of view of toxicity mechanisms. The optimal support vector machine (SVM) model (C = 253.8 and γ = 0.009) was obtained with the genetic algorithm. The SVM classification model produced a prediction accuracy of 89.1% for the training set (451 log1/IBC50), of 80.0% for the test set (150 log1/IBC50), and of 86.9% for the total data set (601 log1/IBC50), which are higher than that (80.5%, 76%, and 79.4%, respectively) from the binary logistic regression (BLR) model. The global SVM classification model is successful, although it deals with a large data set in relation to the toxicity of organics to Vibrio fischeri.

Funder

Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration

Hunan Provincial Natural Science Foundation

Publisher

MDPI AG

Subject

Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science

Reference23 articles.

1. QSAR models for predicting additive and synergistic toxicities of binary pesticide mixtures on Scenedesmus obliquus;Mo;Chin. J. Struct. Chem.,2022

2. Assessment of commonly used pesticides and frequency of self-reported symptoms on farmers health in Kura, Kano State, Nigeria;Isah;J. Educ. Learn. Manag.,2020

3. Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes;Yu;Aquat. Toxicol.,2022

4. Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models;Yu;Ecotoxicol. Environ. Saf.,2020

5. MOA-based linear and nonlinear QSAR models for predicting the toxicity of organic chemicals to Vibrio fischeri;Zhang;Environ. Sci. Pollut. Res.,2020

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri;Archives of Environmental Contamination and Toxicology;2023-07

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