ArticleMolecular therapy. Nucleic acids2026
Fast activity prediction of chemically modified siRNAs via structure-based energy calculations and inference-augmented tabular deep learning.
Article in Molecular therapy. Nucleic acids, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Chemical modification is essential for the clinical application of small interfering RNAs (siRNAs), as it improves their stability and specificity. However, predicting the activity of chemically modified siRNAs remains challenging owing to the scarcity of high-quality datasets and the computational expense of molecular dynamics (MD) simulations. In this study, we propose fast and robust activity prediction of chemically modified siRNAs via structure-based energy (FRAMEs), a novel framework that combines rapid structural prediction via deep learning with physics-based energy calculations for feature engineering of siRNA modifications. To address data scarcity, FRAMEs employs inference-augmented tabular deep learning to achieve robust activity prediction. The total energy score correlates strongly with experimental IC
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