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Artificial intelligence for RNA-ligand interaction prediction: advances and prospects
Jing Li1, Yi Tan1, Ruiqiang Lu1
1Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Science, Macao Polytechnic University, 999078 Macao, China.
Drug Discovery Today
|April 26, 2025
Summary
Artificial intelligence (AI) is transforming RNA-ligand interaction studies by improving prediction accuracy for drug discovery. This review covers AI applications in binding site identification, modeling, and screening, addressing current challenges and future directions.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- RNA-ligand interactions are crucial for biological processes and therapeutic development.
- Predicting these interactions is complex, necessitating advanced computational approaches.
- Artificial intelligence (AI) offers powerful tools to analyze RNA-ligand dynamics and potential.
Purpose of the Study:
- To review the advancements in AI-driven methods for RNA-ligand interaction prediction.
- To highlight AI applications in binding site identification, structure modeling, binding mode, and affinity prediction.
- To discuss challenges and future research directions in the field.
Main Methods:
- Review of current literature on AI applications in RNA-ligand interaction studies.
- Analysis of AI techniques for binding site identification, structure modeling, and virtual screening.
- Discussion of challenges including data scarcity and RNA flexibility modeling.
Main Results:
- AI significantly enhances the accuracy of RNA-ligand binding site identification and structure prediction.
- AI-driven virtual screening accelerates the discovery of potential RNA-targeted drugs.
- Key challenges remain in data availability and accurately modeling RNA dynamics.
Conclusions:
- AI is revolutionizing RNA-ligand interaction prediction, crucial for biological understanding and drug discovery.
- Future work should integrate AI with physics-based models and expand experimental datasets.
- Enhanced prediction capabilities promise to accelerate the development of novel RNA-targeted therapeutics.
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