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

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 sp...

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Bibliographic Details
Published in:	Journal of cheminformatics Vol. 14; no. 1; p. 4
Main Authors:	Grimberg, Hadar, Tiwari, Vinay S., Tam, Benjamin, Gur-Arie, Lihi, Gingold, Daniela, Polachek, Lea, Akabayov, Barak
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 02-02-2022 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Antibiotics Chemical biology Chemical synthesis Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Data science Documentation and Information in Chemistry Inhibitors Learning algorithms Machine learning Research Article Ribonucleic acid RNA rRNA Small-molecule inhibitors Targeting RNA Theoretical and Computational Chemistry Tuberculosis Chemical biology Antibiotics Small-molecule inhibitors Targeting RNA Machine learning
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Summary:	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
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1758-2946 1758-2946
DOI:	10.1186/s13321-022-00583-x