The problem of designing new antitubercular drugs against multiple drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR‐TB, we collected a large literature data set and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q² = .7–.8 (regression models) and balanced accuracies of about 80% (classification models) with cross‐validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR‐TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR‐TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti‐TB activity of new chemicals.

中文翻译：

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具有抗结核活性的异烟酸酰肼衍生物的合理设计：机器学习，分子对接，合成和生物学测试

使用先进的机器学习方法解决了针对多药耐药结核病（MDR-TB）设计新的抗结核药物的问题。由于针对MDR-TB的已发表的测量数据很少，因此我们收集了大量文献数据集并开发了针对非耐药性H37Rv菌株的模型。这些模型的预测准确性具有确定系数q ² = .7–.8（回归模型）和约80％的平衡精度（分类模型），带有交叉验证和独立测试集。这些模型用于筛选虚拟化学库，该库旨在具有MDR-TB活性。鉴定，合成和测试了七个最有前途的化合物。它们都显示出针对H37Rv菌株的活性，三个分子显示出针对MDR-TB菌株的活性。对接分析表明，发现的分子可结合分枝杆菌细胞壁发育所需的烯酰还原酶InhA。这些模型可在线免费获得（http://ochem.eu/article/103868），可用于预测新化学品的潜在抗结核活性。