RNA design method improves 3D folding using geometry and latent diffusion
GOLD: Geometric Optimized Latent Diffusion for Structure-Aware RNA Inverse Folding
Artificial Intelligence
Summary
Designing RNA molecules that fold correctly into specific shapes is important for making new medicines. The authors created a new AI method called GOLD that combines discrete sequence data with continuous 3D angle information to better predict RNA folding. This method uses a smooth mathematical space to generate RNA sequences that are more likely to fold into stable 3D structures. They also added a way to check physical constraints during generation to guide the design towards realistic structures.
What this means in practice
- •For biotech engineers: Generate RNA sequences that more reliably fold into target 3D shapes to speed up therapeutic RNA design.
- •For computational drug developers: Produce structurally valid RNA molecules to improve stability and function in RNA-based drug candidates.
Authors
Qi Si, Xuyang Liu, Penglei Wang, Shuo Su, Limei Han, Mengzhu Li, Xin Guo, Yuan Qi, Yuan Cheng
Abstract
Designing RNA sequences that fold into specific 3D structures is a fundamental challenge in therapeutics. Current RNA inverse folding methods typically condition on 3D backbone coordinates but the generation is limited to 1D discrete sequences. This ignores the continuous side-chain orientations, which are essential for overall structural stability. To address this issue, we explicitly introduce side-chain geometry into the RNA generative process. Unlike co-design frameworks that diffuse across separate raw spaces, we propose Geometric Optimized Latent Diffusion (\textbf{GOLD}), which project sequence and structure into a unified latent manifold. GOLD employs a novel GeoVAE to elegantly integrate discrete RNA sequences with continuous side-chain angles. By mapping these modalities into a single, smooth latent manifold, GOLD naturally facilitates a synergistic co-diffusion process. To enhance structural validity, we further introduce a training-free, gradient-guided sampling mechanism. By decoding the generated sequence probabilities and side-chain geometries into structures during the sampling process, we compute analytical gradients from physical constraints. This dynamic feedback actively guides the generated sequence to a physically stable state. Extensive experiments validate the superiority of GOLD, achieving state-of-the-art performance across sequence recovery, secondary structure validity, and 3D foldability metrics. Our code and data are publicly available at https://github.com/darkflash03/GOLD.