Rational design of isonicotinic acid hydrazide derivatives with anti-tubercular activity: Machine learning, molecular docking, synthesis and biological testing

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.

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The design of new anti-tubercular drugs against multiple-drug-resistant tuberculosis (MDR-TB) was addressed using advanced machine learning methods, such as Associative Neural Networks and Xgboost. The activity of synthesised molecules selected from a virtual chemical library was confirmed in prospective studies. The data and developed models are publicly available at On-line Chemical Database and Modelling (OCHEM) environment http://ochem.eu.

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