Machine learning and generative AI in the rational design of DNA gyrase-targeted antibacterials

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling Pub Date : 2025-09-22 DOI:10.1016/j.jmgm.2025.109178

Krishnamurthy Ganga Gayathri

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Abstract

DNA gyrase, a critical bacterial enzyme, was targeted using an AI-driven approach to accelerate antibacterial drug discovery. Machine learning (ML) models, including Gradient Boosting Regressor (GBR) and XGBoost, were optimized on pIC₅₀ data, with GBR achieving superior generalization (train R² = 0.84, test R² = 0.76). A Graph Convolutional Network Variational Autoencoder (GCN-VAE) generated diverse molecular scaffolds, validated by Tanimoto similarity. Out of 100 AI-generated drug-like molecules, 11 were identified as structurally unique, with one (denoted as DR7) exhibiting a predicted pIC₅₀ > 7, indicating potent inhibitory activity. Docking studies of DR7 with DNA gyrase identified a lead molecule with a binding energy of −7.44 kcal/mol and inhibition constant (K_i) of 3.52 μM. Key protein-ligand interactions, involving TYR86 and ASP87, were highlighted alongside electronic characterization of HOMO and LUMO distributions, elucidating binding potential. This integrative framework of ML, generative AI, and molecular docking offers a transformative path for developing DNA gyrase inhibitors and advancing antibacterial therapeutics.

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机器学习和生成式人工智能在DNA旋回靶向抗菌药合理设计中的应用

DNA回转酶是一种关键的细菌酶，利用人工智能驱动的方法来加速抗菌药物的发现。在pIC50数据上对机器学习（ML）模型（包括梯度增强回归器（Gradient Boosting Regressor， GBR）和XGBoost）进行了优化，GBR的泛化效果较好（训练R2 = 0.84，检验R2 = 0.76）。图卷积网络变分自编码器（GCN-VAE）生成了多种分子支架，并通过谷本相似度验证。在100个人工智能生成的药物样分子中，有11个被鉴定为结构独特，其中一个（表示为DR7）显示出预测的pIC50 >； 7，表明有强大的抑制活性。DR7与DNA旋切酶的对接研究发现，该先导分子结合能为−7.44 kcal/mol，抑制常数（Ki）为3.52 μM。重点研究了包括TYR86和ASP87在内的关键蛋白质配体相互作用，以及HOMO和LUMO分布的电子表征，阐明了结合势。这种机器学习、生成式人工智能和分子对接的整合框架为开发DNA回转酶抑制剂和推进抗菌治疗提供了一条变革性的途径。

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来源期刊

Journal of molecular graphics & modelling 生物-计算机：跨学科应用

CiteScore

5.50

自引率

6.90%

发文量

216

审稿时长

35 days

期刊介绍： The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.