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

Authors :
Feng Wu
Xinhua Zhang
Zhengjun Fang
Xinliang Yu
Source :
Molecules, Vol 28, Iss 6, p 2703 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
14203049
Volume :
28
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Molecules
Publication Type :
Academic Journal
Accession number :
edsdoj.6882c1e1929940c28391aee43e56ea08
Document Type :
article
Full Text :
https://doi.org/10.3390/molecules28062703