Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods

Abstract

In this study, we developed new computational DNA adduct prediction models by using significantly more diverse training data-set of 217 DNA adducts and 1024 non-DNA adducts, and applying five machine learning methods which include support vector machine (SVM), k-nearest neighbour, artificial neural networks, logistic regression and continuous kernel discrimination. The molecular descriptors used for DNA adduct prediction were selected from a pool of 548 descriptors by using a multi-step hybrid feature selection method combining Fischer-score and Monte Carlo simulated annealing method. Some of the selected descriptors are consistent with the structural and physicochemical properties reported to be important for DNA adduct formation. The y-scrambling method was used to test whether there is a chance correlation in the developed SVM model. In the meantime, fivefold cross-validation of these machine learning methods results in the prediction accuracies of 64.1–82.5% for DNA adducts and 95.1–97.6% for non-DNA adducts, and the prediction accuracies for external test set are 78.2–100% for DNA adducts and 92.6–98.4% for non-DNA adducts. Our study suggested that the tested machine learning methods are potentially useful for DNA adducts identification.

Keywords:

• machine learning method
• DNA adducts
• feature selection
• Monte Carlo simulated annealing
• applicability domain

Acknowledgements

This study was supported by a grant from the Two-Way Support Programs of Sichuan Agricultural University (Project No. 00770117) and the National Natural Science Foundation of China (Project No. 20973118).