Identification of dual-targeted Mycobacterium tuberculosis aminoacyl-tRNA synthetase inhibitors using machine learning

Recently, the article dedicated to the development of novel antituberculosis agents targeting aminoacyl-tRNA synthetases using machine learning approach has been published.

Abstract:

Background: The most serious challenge in the treatment of tuberculosis is the multidrug resistance of Mycobacterium tuberculosis to existing antibiotics. As a strategy to overcome resistance we used a multitarget drug design approach. The purpose of the work was to discover dual-targeted inhibitors of mycobacterial LeuRS and MetRS with machine learning. Methods: The artificial neural networks were built using module nnet fro R 3.6.1. The inhibitory activity of compounds toward LeuRS and MetRS was investigated in aminoacylation assay. Results: Using a machine-learning approach, we identified dual-targeted inhibitors of LeuRS and MetRS among 2-(quinolin-2-ylsulfanyl)-acetamide derivatives. The most active compounds inhibits MetRS and LeuRS with IC50 values of 33 mM and 23.9 mM, respectively. Conclusion: 2-(Quinolin-2-ylsulfanyl)-acetamide scaffold can be useful for further research.  

• Identification of dual-targeted Mycobacterium tuberculosis aminoacyl-tRNA synthetase inhibitors using machine learning. Volynets GP, Usenko MO, Gudzera OI, Starosyla SA, Balanda AO, Syniugin AR, Gorbatiuk OB, Prykhod'ko AO, Bdzhola VG, Yarmoluk SM, Tukalo MA. Future Med. Chem. 2022, 14(17):1223-1237.