PINNACLE · Physics-Informed Neural Networks for Accurate Computational Learning of RNA Elements
„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“
- Период
- 2026-06-15 → 2028-06-14
- Финансиране от ЕС
- 263 393 €
- Участници
- 2
- Схема
- HORIZON-TMA-MSCA-PF-EF
Линиите свързват координатора с партньорите.
Накратко на български
РНК молекулите и тяхната сложна триизмерна структура се анализират чрез невронни мрежи, в които са вградени физични закони. Това помага за по-точното предсказване на формите на РНК, което ускорява биологичните изследвания и разработването на нови терапии.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Цел на проекта
Ribonucleic acid (RNA) molecules are central to cellular function and disease, with their biological roles being intrinsically linked to their complex three-dimensional (3D) structures. 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 the dependency on large training sets, improve generalization to novel RNA families, and provide robust uncertainty quantification for its predictions - a critical feature for guiding experimental research. This action will deliver a transformative open-source tool for the scientific community, accelerating research in RNA biology and the development of RNA-based therapeutics. Furthermore, this interdisciplinary training will equip the researcher with a unique skill-set at the intersection of AI, biophysics, and bioinformatics, providing a robust foundation for a future independent research career.
Оригинален текст от CORDIS (на английски).
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Данни: CORDIS, © Европейски съюз