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Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods

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Abstract

Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for countering drug-resistant bacteria. Bacterial DNA gyrase, functioning as an ATP-dependent enzyme, plays a pivotal role in the intricate processes of transcription, replication, and chromosome segregation within bacterial DNA. This renders it a prime target for the development of innovative antibacterial agents. However, the experimental identification of bacterial DNA gyrase inhibitors faces multifaceted challenges due to current methodological constraints. Recognizing its significance, this study developed 56 computational models designed for predicting bacterial DNA gyrase inhibitors. These models employed seven distinct molecular fingerprints and eight machine learning algorithms. Among these models, Model_2D, created using KlekotaRoth fingerprints and the SVM algorithm, stands out as the most robust performer (ACC = 0.86, MCC = 0.63, G-mean = 0.82). Moreover, given the limited exploration of structural fragments required for DNA Gyrase B inhibitors, crucial structural fingerprints influencing DNA Gyrase B inhibitors were identified through Bayesian classification. Subsequently, we conducted molecular docking to reveal the binding modes between these crucial structural fingerprints and the active site of DNA gyrase B. In conclusion, the present study aimed to develop the optimal classification model for bacterial DNA gyrase inhibitors, offering invaluable support to medicinal chemists creating innovative DNA gyrase inhibitors.

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Acknowledgements

Acknowledgement: This work is financially supported by the National Science and Technology Major Project of China (Grant No. 2019ZX09201004-001), and the National Natural Science Foundation of China (Grant No. 81530100 and Grant No. U1603285).

Funding

This article was funded by National Science and Technology Major Project of China, Grant No. 2019ZX09201004-001, Grant No. 2019ZX09201004-001, National Natural Science Foundation of China, Grant No. 81530100 and Grant No. U1603285, Grant No. 81530100 and Grant No. U1603285.

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  1. State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Guozheng Zhou & Yan Li

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  1. Guozheng Zhou
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Guozheng Guo: Conceptualization, Formal analysis, Data Curation, Writing - Original Draft, Visualization Yan Li: Writing - Review & Editing, Funding acquisition

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Zhou, G., Li, Y. Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10806-y

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