Rational design of isonicotinic acid hydrazide derivatives with anti‐tubercular activity: Machine learning, molecular docking, synthesis and biological testing
Vasyl Kovalishyn; Julie Grouleff; Ivan Semenyuta; Vitaliy O. Sinenko; Sergiy R. Slivchuk; Diana Hodyna; Volodymyr Brovarets; Volodymyr Blagodatny; Gennady Poda; Igor V. Tetko; Larysa Metelytsia
Index: 10.1111/cbdd.13188
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Abstract
The problem of designing new anti‐tubercular drugs against multiple‐drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. Since there are only few published measurements against MDR‐TB, we collected a large literature dataset and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 = 0.7‐0.8 (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.