arXiv:2512.03592v1 Announce Type: new 
Abstract: The RNA inverse folding problem, a key challenge in RNA design, involves identifying nucleotide sequences that can fold into desired secondary structures, which are critical for ensuring molecular stability and function. The inherent complexity of this task stems from the intricate relationship between sequence and structure, making it particularly challenging. In this paper, we propose a framework, named HyperRNA, a generative model with an encoder-decoder architecture that leverages hypergraphs to design RNA sequences. Specifically, our HyperRNA model consists of three main components: preprocessing, encoding and decoding.
  In the preprocessing stage, graph structures are constructed by extracting the atom coordinates of RNA backbone based on 3-bead coarse-grained representation. The encoding stage processes these graphs, capturing higher order dependencies and complex biomolecular interactions using an attention embedding module and a hypergraph-based encoder. Finally, the decoding stage generates the RNA sequence in an autoregressive manner. We conducted quantitative and qualitative experiments on the PDBBind and RNAsolo datasets to evaluate the inverse folding task for RNA sequence generation and RNA-protein complex sequence generation. The experimental results demonstrate that HyperRNA not only outperforms existing RNA design methods but also highlights the potential of leveraging hypergraphs in RNA engineering.

تم تقديم إطار عمل جديد يسمى HyperRNA لمعالجة مشكلة الطي العكسي للحمض النووي الريبي (RNA)، وهو أمر حاسم لتصميم RNA. يستخدم هذا الإطار الرسوم البيانية الفائقة ضمن نموذج توليدي يتميز بهندسة تشفير-فك تشفير لتحديد تسلسلات النوكليوتيدات التي يمكن أن تحقق هياكل ثانوية مرغوبة، مما يعزز الاستقرار والوظيفة الجزيئية.

Se ha introducido un nuevo marco llamado HyperRNA para abordar el problema del plegamiento inverso del ARN, que es crucial para el diseño de ARN. Este marco emplea hipergrafos dentro de un modelo generativo con una arquitectura de codificador-decodificador para identificar secuencias de nucleótidos que pueden lograr estructuras secundarias deseadas, mejorando así la estabilidad y función molecular.

Un nouveau cadre nommé HyperRNA a été introduit pour s'attaquer au problème du repliement inverse de l'ARN, qui est crucial pour la conception de l'ARN. Ce cadre utilise des hypergraphes dans un modèle génératif doté d'une architecture encodeur-décodeur pour identifier des séquences de nucléotides capables d'atteindre des structures secondaires souhaitées, améliorant ainsi la stabilité et la fonction moléculaire.

A new framework named HyperRNA has been introduced to tackle the RNA inverse folding problem, which is crucial for RNA design. This framework employs hypergraphs within a generative model featuring an encoder-decoder architecture to identify nucleotide sequences that can achieve desired secondary structures, enhancing molecular stability and function.

Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding

طور الباحثون مفاتيح جزيئية من الحمض النووي الريبي باستخدام آلات بولتزمان المقيدة، كما ورد في مجلة Nature — Machine Learning. تهدف هذه الطريقة المبتكرة إلى تحسين تصميم ووظائف جزيئات الحمض النووي الريبي، التي تعتبر حيوية لعمليات بيولوجية متعددة.

Investigadores han desarrollado interruptores moleculares de ARN utilizando máquinas de Boltzmann restringidas, según se informa en Nature — Machine Learning. Este enfoque innovador tiene como objetivo mejorar el diseño y la funcionalidad de las moléculas de ARN, que son cruciales para varios procesos biológicos.

Des chercheurs ont développé des commutateurs moléculaires d'ARN en utilisant des machines de Boltzmann restreintes, comme rapporté dans Nature — Machine Learning. Cette approche innovante vise à améliorer la conception et la fonctionnalité des molécules d'ARN, qui sont cruciales pour divers processus biologiques.

Researchers have developed molecular RNA switches using Restricted Boltzmann machines, as reported in Nature — Machine Learning. This innovative approach aims to enhance the design and functionality of RNA molecules, which are crucial for various biological processes.

Harnessing Hypergraphs in Geometric Deep Learning for 3D RNA Inverse Folding

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