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

Future Med Chem. 2022 Sep;14(17):1223-1237. doi: 10.4155/fmc-2022-0085. Epub 2022 Jul 25.


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 from R 3.6.1. The inhibitory activity of compounds toward LeuRS and MetRS was investigated in aminoacylation assays. 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 compound inhibits MetRS and LeuRS with IC50 values of 33 μm and 23.9 μm, respectively. Conclusion: 2-(Quinolin-2-ylsulfanyl)-acetamide scaffold can be useful for further research.

Trial registration: NCT03557281.

Keywords: 2-(quinolin-2-ylsulfanyl)-acetamide; Mycobacterium tuberculosis; artificial neural network; leucyl-tRNA synthetase; machine learning; methionyl-tRNA synthetase.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Acetamides / therapeutic use
  • Amino Acyl-tRNA Synthetases* / therapeutic use
  • Humans
  • Machine Learning
  • Mycobacterium tuberculosis*
  • Tuberculosis* / drug therapy
  • Tuberculosis* / microbiology


  • Acetamides
  • Amino Acyl-tRNA Synthetases

Associated data