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Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing
Author(s) -
Kovalishyn Vasyl,
Grouleff Julie,
Semenyuta Ivan,
Sinenko Vitaliy O.,
Slivchuk Sergiy R.,
Hodyna Diana,
Brovarets Volodymyr,
Blagodatny Volodymyr,
Poda Gennady,
Tetko Igor V.,
Metelytsia Larysa
Publication year - 2018
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13188
Subject(s) - inha , docking (animal) , virtual screening , quantitative structure–activity relationship , computational biology , hydrazide , isonicotinic acid , chemistry , drug discovery , cheminformatics , molecular descriptor , mycobacterium tuberculosis , computer science , stereochemistry , combinatorial chemistry , machine learning , biochemistry , biology , tuberculosis , computational chemistry , medicine , organic chemistry , nursing , pathology
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 q 2 = .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.