RNA-targeted small-molecule drug discoveries: a machine-learning perspective

Huan Xiao, Xin Yang, Yihao Zhang, Zongkang Zhang, Ge Zhang*, Bao-Ting Zhang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)
19 Downloads (Pure)


In the past two decades, machine learning (ML) has been extensively adopted in protein-targeted small molecule (SM) discovery. Once trained, ML models could exert their predicting abilities on large volumes of molecules within a short time. However, applying ML approaches to discover RNA-targeted SMs is still in its early stages. This is primarily because of the intrinsic structural instability of RNA molecules that impede the structure-based screening or designing of RNA-targeted SMs. Recently, with more studies revealing RNA structures and a growing number of RNA-targeted ligands being identified, it resulted in an increased interest in the field of drugging RNA. Undeniably, intracellular RNA is much more abundant than protein and, if successfully targeted, will be a major alternative target for therapeutics. Therefore, in this context, as well as under the premise of having RNA-related research data, ML-based methods can get involved in improving the speed of traditional experimental processes. (Figure presented.).

Original languageEnglish
Pages (from-to)384-397
Number of pages14
JournalRNA Biology
Issue number1
Publication statusPublished - Jan 2023

Scopus Subject Areas

  • Molecular Biology
  • Cell Biology

User-Defined Keywords

  • RNA
  • deep learning
  • drug discovery
  • machine learning
  • microRNA
  • small molecule


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