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Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.

Publication ,  Journal Article
Bagnolini, G; Luu, TB; Hargrove, AE
Published in: RNA (New York, N.Y.)
April 2023

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

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Published In

RNA (New York, N.Y.)

DOI

EISSN

1469-9001

ISSN

1355-8382

Publication Date

April 2023

Volume

29

Issue

4

Start / End Page

473 / 488

Related Subject Headings

  • RNA
  • Neural Networks, Computer
  • Machine Learning
  • Developmental Biology
  • 3101 Biochemistry and cell biology
  • 0601 Biochemistry and Cell Biology
 

Citation

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Bagnolini, G., Luu, T. B., & Hargrove, A. E. (2023). Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA (New York, N.Y.), 29(4), 473–488. https://doi.org/10.1261/rna.079497.122
Bagnolini, Greta, TinTin B. Luu, and Amanda E. Hargrove. “Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.RNA (New York, N.Y.) 29, no. 4 (April 2023): 473–88. https://doi.org/10.1261/rna.079497.122.
Bagnolini, Greta, et al. “Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.RNA (New York, N.Y.), vol. 29, no. 4, Apr. 2023, pp. 473–88. Epmc, doi:10.1261/rna.079497.122.

Published In

RNA (New York, N.Y.)

DOI

EISSN

1469-9001

ISSN

1355-8382

Publication Date

April 2023

Volume

29

Issue

4

Start / End Page

473 / 488

Related Subject Headings

  • RNA
  • Neural Networks, Computer
  • Machine Learning
  • Developmental Biology
  • 3101 Biochemistry and cell biology
  • 0601 Biochemistry and Cell Biology