Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

Hadar Grimberg, Vinay S. Tiwari, Benjamin Tam, Lihi Gur-Arie, Daniela Gingold, Lea Polachek, Barak Akabayov

Research output: Contribution to journalArticlepeer-review


In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, and chemical features that enhance the binding to the targeted RNA. Functional validation was conducted after synthesizing 10 small molecules pinpointed computationally. Four of the 10 were found to be potent inhibitors that target hairpin 91 in the ribosomal PTC of M. tuberculosis and, as a result, stop translation. Graphical Abstract: [Figure not available: see fulltext.]

Original languageAmerican English
Article number4
JournalJournal of Cheminformatics
Issue number1
StatePublished - 1 Dec 2022


  • Antibiotics
  • Chemical biology
  • Machine learning
  • Small-molecule inhibitors
  • Targeting RNA

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Library and Information Sciences


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