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QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine

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Abstract

Studies on the interactions of antibiotic compounds with DNA can provide useful suggestions and guidance for the design of new and more efficient DNA-binding drugs. A quantitative structure–activity relationship (QSAR) study of the binding modes and binding affinities of the interactions between 30 antibiotic compounds and DNA was performed. A large number of descriptors that encode hydrophobic, topological, geometrical, and electronic properties were calculated to represent the structures of the antibiotic compounds. Aiming at a system with small, multidimensional samples, we utilized the genetic algorithm-support vector machine (GA-SVM) method to develop the QSAR, which can select an optimized feature subset and optimize SVM parameters simultaneously. A binary QSAR model for predicting binding mode and conventional QSAR models for predicting binding affinity were built based on the GA-SVM approach. The descriptors selected using GA-SVM represented the overall descriptor space and can account well for the binding nature of the considered dataset. The descriptors selected using the GA-SVM method were then used for developing conventional QSAR models by the artificial neural network (ANN) approach. A comparison between the conventional QSAR models using GA-SVM with those using ANN revealed that the former were much better. GA-SVM models can be useful for predicting binding modes and binding activities of the interactions of new antibiotic compounds with DNA.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 20945003, 21005063), and the Natural Science Foundation of Gansu (No. 096RJZA121).

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Authors and Affiliations

  1. Key Laboratory of Bioelectrochemistry and Environmental Analysis of Gansu Province, College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xi Bin Zhou, Wen Jing Han, Jing Chen & Xiao Quan Lu

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  1. Xi Bin Zhou
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  2. Wen Jing Han
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  3. Jing Chen
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  4. Xiao Quan Lu
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Correspondence to Xiao Quan Lu.

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Zhou, X.B., Han, W.J., Chen, J. et al. QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine. Monatsh Chem 142, 949–959 (2011). https://doi.org/10.1007/s00706-011-0493-7

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  • DOI: https://doi.org/10.1007/s00706-011-0493-7

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