KinPFN for RNA folding kinetics
KinPFN uses prior-data fitted networks to approximate first-passage-time distributions for RNA folding kinetics orders of magnitude faster than direct simulation.
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RNA-protein complex refinement using AI modeling and docking
This article explains a workflow for refining protein-RNA complexes by combining AI-based structural models with flexible docking and enhanced sampling.
RNA recognition by Musashi-1
Molecular dynamics and binding-energy calculations are used here to compare how Musashi-1 recognizes different RNA motifs and to identify determinants of binding specificity.
Caveats in deep learning for RNA secondary structure prediction
This paper shows that many deep learning models for RNA secondary structure prediction learn dataset bias more readily than RNA folding rules, and explains why that matters for the future of AI in RNA biology.
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