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DeepMIF: A Multiview Interactive Fusion-Based Deep Learning Method for RNA-Small Molecule Binding Affinity

Jinmiao Song1,2, Annan Gao1,2, Shengwei Tian1,2

  • 1School of Software, Xinjiang University, Urumqi 830091, China.

Journal of Chemical Information and Modeling
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

DeepMIF accurately predicts RNA-small molecule binding affinity using a novel deep learning approach. This method enhances drug discovery by effectively integrating diverse molecular features for better interaction modeling.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Predicting RNA-small molecule binding affinity (RSMA) is vital for drug discovery.
  • Current computational methods struggle with multisource feature integration and complex interaction modeling.

Purpose of the Study:

  • To present DeepMIF, a deep learning framework for improved RSMA prediction.
  • To address limitations in existing computational methods for RNA-targeted drug discovery.

Main Methods:

  • Developed DeepMIF, a deep learning framework utilizing a multiview interactive fusion paradigm.
  • Employed a hybrid RNA representation (L-ESKmer and pretrained embeddings) and dual small molecule features (sequence and graph).
  • Implemented a multihead cross-attention network for intelligent information synthesis across modalities.

Main Results:

  • Achieved state-of-the-art performance on a benchmark dataset (1439 pairs) with PCC of 0.796 and RMSE of 0.874.
  • Demonstrated strong generalization and robustness in cold-start scenarios.
  • Interpretability analysis confirmed the capture of biologically relevant binding sites.

Conclusions:

  • DeepMIF offers a powerful new approach for predicting RNA-small molecule binding affinity.
  • The framework shows significant potential to guide structure-based RNA-targeted drug design.
  • DeepMIF's ability to model complex interactions advances computational drug discovery.