2. Academic Validation
  3. Predicting small molecule-RNA interactions without RNA tertiary structures

Predicting small molecule-RNA interactions without RNA tertiary structures

  • Nat Biotechnol. 2026 Jan 2. doi: 10.1038/s41587-025-02942-z.
Yuhan Fei # 1 2 3 4 5 6,  Pengfei Wang # 1 2 3 4 5 6,  Jiasheng Zhang # 1 2 3 4 5 6,  Xinyue Shan 1 2 3 4 5 6,  Zilin Cai 1 2 3 4 5 6,  Jianbo Ma 1 2 3 4 5 6,  Yangming Wang 7 8 9 10,  Qiangfeng Cliff Zhang 11 12 13 14 15 16
Affiliations

Affiliations

  • 1 State Key Laboratory of Membrane Biology, Tsinghua University, Beijing, China.
  • 2 MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
  • 3 Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
  • 4 Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China.
  • 5 School of Life Sciences, Tsinghua University, Beijing, China.
  • 6 Tsinghua-Peking Center for Life Sciences, Beijing, China.
  • 7 Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, China. [email protected].
  • 8 State Key Laboratory of Gene Function and Modulation Research, Peking University, Beijing, China. [email protected].
  • 9 Beijing Advanced Center of RNA Biology (BEACON), Peking University, Beijing, China. [email protected].
  • 10 Southwest United Graduate School, Kunming, China. [email protected].
  • 11 State Key Laboratory of Membrane Biology, Tsinghua University, Beijing, China. [email protected].
  • 12 MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China. [email protected].
  • 13 Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China. [email protected].
  • 14 Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China. [email protected].
  • 15 School of Life Sciences, Tsinghua University, Beijing, China. [email protected].
  • 16 Tsinghua-Peking Center for Life Sciences, Beijing, China. [email protected].
  • # Contributed equally.
PMID: 41482542 DOI: 10.1038/s41587-025-02942-z
Abstract

Small molecules can bind RNAs to regulate their fate and functions, providing promising opportunities for treating human diseases. However, current tools for predicting small molecule-RNA interactions (SRIs) require prior knowledge of RNA tertiary structures. Here we present SMRTnet, a deep learning method that uses multimodal data fusion to integrate two large language models with convolutional and graph attention networks to predict SRIs on the basis of RNA secondary structure. SMRTnet achieves high performance across multiple experimental benchmarks, substantially outperforming existing tools. SMRTnet predictions for ten disease-associated RNA targets identified 40 hits of RNA-targeting small molecules with nanomolar-to-micromolar dissociation constants. Focusing on the MYC internal ribosome entry site, SMRTnet-predicted small molecules showed binding scores correlated closely with observed validation rates. One predicted small molecule downregulated MYC expression, inhibited proliferation and promoted Apoptosis in three Cancer cell lines. Thus, by eliminating the need for RNA tertiary structures, SMRTnet expands the scope of feasible RNA targets and accelerates the discovery of RNA-targeting therapeutics.

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