Background: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis
(Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs.
Objective: The goal of this work is to identify potent small molecule anti-TB agents by machine learning,
synthesis and biological evaluation.
Methods: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive
machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular
activity against H37Rv and resistant Mtb strains.
Results: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid
(INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the
models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61–78% for
the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti-
TB activity with a reasonable accuracy (with BA = 67–79 %) within the applicability domain. Seven designed
compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant
(MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation
in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of
10−100 mg/L) and one as practically harmless (LD50 in the range of 100−1000 mg/L).
Conclusion: The newly identified compounds may represent a starting point for further development of
therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11
1066 and can be used to virtually screen for potential compounds with anti-TB activity.