Dresden 2026 – scientific programme
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BP: Fachverband Biologische Physik
BP 14: Poster Session II
BP 14.57: Poster
Tuesday, March 10, 2026, 18:00–21:00, P2
Exploring coarse graining RNA force fields via Machine Learning — •Anton Emil Dorn1, Emile de Bruyn1, Fabrice von der Lehr3, Stefan Kesselheim1,4, Philipp Knechtges3, and Alexander Schug2 — 1Forschungszentrum Jülich — 2KIT — 3DLR Köln — 4Universität zu Köln
In Protein structure prediction there have been massive improvements recently with the help of machine learning. In RNA structure prediction however the situation is less ideal due too much sparser experimental data. Here we attempt to solve a modified version of the problem by determining a coarse-grained RNA force field for Molecular Dynamics simulations. The data sparsity can here be alleviated by atomistic RNA simulations using proven and established force fields. In a first step we show the viability of this approach with a limited scenario of only small RNA molecules. We also explore different bead numbers for the coarse graining to determine the best approximation.
Keywords: RNA; Machine Learning; Molecular Dynamics Simulation; Force Field