ArticleCommunications biology2025
Targeting RNA with small molecules using state-of-the-art methods provides highly predictive affinities of riboswitch inhibitors.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- HCV-Induced Hepatocarcinogenesis: Molecular Mechanisms, Persistent Cancer Risk, and Future Perspectives.Biomedicines · 2026Review
- Critical Assessment of a Structure-Based Pipeline for Targeting the Long Noncoding RNA MALAT1.Journal of chemical information and modeling · 2026Article
- Artificial intelligence and machine learning in immunosenescence: from biomarker discovery to clinical translation.Frontiers in aging · 2026Review
- Relative Binding Free Energy Estimation of Congeneric Ligands and Macromolecular Mutants with the Alchemical Transfer Method with Coordinate Swapping.Journal of chemical information and modeling · 2025Article
- Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.Journal of chemical information and modeling · 2024Article
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Authors and funding
9 authors.
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Abstract
Targeting RNA with small molecules represents a promising yet relatively unexplored avenue for the design of new drugs. Nevertheless, challenges arise from the lack of computational models and techniques able to accurately model RNA systems, and predict their binding affinities to small molecules. Here, we tackle these difficulties by developing a tailored state-of-the-art approach for absolute binding free energy calculations of RNA-binding small molecules. For this, we combine the advanced AMOEBA polarizable force field to the newly developed lambda-Adaptive Biasing Force scheme associated to refined restraints allowing for efficient sampling. To capture the free energy barrier associated to challenging RNA conformational changes, we combine machine learning-based collective variables with enhanced sampling simulations. Applying this computational protocol to a complex Riboswitch-like RNA target demonstrates quantitative predictions. These results pave the way for the routine application of free energy simulations in RNA-targeted drug discovery, thus providing a significant reduction in their failure rate.
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Registered trials
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