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Physics-Informed Neural Networks for Accurate Computational Learning of RNA Elements

CORDIS · observation · Publication date unknown

ructures. However, a significant gap exists between the number of known RNA sequences and experimentally determined structures. This data scarcity severely hampers the application of state-of-the-art deep learning methods, which have revolutionized protein structure prediction but fail to generalize for RNA due to their reliance on vast datasets. The PINNACLE (Physics-Informed Neural Networks for Accurate Computational Learning of RNA Elements) project will address this fundamental challenge through a novel, physics-aware computational framework. The project has two primary objectives: 1) To construct and disseminate a high-quality, FAIR-compliant, and systematically curated dataset of RNA 3D structures, augmented with molecular simulations to capture molecular flexibility and rare interactions. 2) To develop and validate a novel Bayesian Physics-Informed Neural Network (B-PINN) that directly embeds the fundamental physical and biochemical laws governing RNA folding (e.g., electrostatics, base-stacking, torsional constraints) into the model's learning process. PINNACLE will reduce

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recordType

award

status

SIGNED

region

EU

value

263393.28

unit

EUR