Geometric Algebra-Enhanced Bayesian Flow Network for RNA Inverse Design 1. The paper introduces RBFN, a novel method for RNA inverse design that combines geometric algebra with Bayesian Flow Networks to generate RNA sequences from 3D structures. This approach addresses the challenge of designing RNA sequences that can fold into specific 3D structures, a critical task in RNA therapeutics and synthetic biology. 2. A key innovation is the use of geometric algebra to enhance the modeling of RNA's 3D structures. By encoding structural information into multivectors, the method captures complex geometric relationships, enabling more accurate and flexible RNA design compared to traditional methods. 3. The Bayesian Flow Network component allows for distribution-based sequence generation, aligning nucleotide distributions rather than generating discrete sequences directly. This probabilistic approach improves the model's ability to explore diverse sequence possibilities and enhances overall design efficiency. 4. RBFN proposes a new time-step sampling distribution tailored for RNA sequences, focusing on the transition from initial to target distributions. This strategy improves the model's global generation ability, particularly important given the limited diversity of RNA nucleotides (A, C, G, U). 5. Extensive experiments demonstrate RBFN's superior performance over state-of-the-art methods, including gRNAde and Rosetta, in both single-state and multi-state RNA design benchmarks. The results highlight significant improvements in sequence recovery rates and structural consistency metrics. 6. The study also includes ablation experiments that validate the contributions of geometric algebra and the new time-step sampling distribution. These components are shown to be crucial for achieving high-quality RNA sequence design with consistent structural properties. 📜Paper: openreview.net/pdf/daf45e7e0… #RNAInverseDesign #GeometricAlgebra #BayesianFlowNetworks #ComputationalBiology #RNAEngineering