7 papers
RiboSphere: Learning Unified and Efficient Representations of RNA Structures
Zhou Zhang, Hanqun Cao, Cheng Tan +3
Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures a…
Preserving Continuous Symmetry in Discrete Spaces: Geometric-Aware Quantization for SO(3)-Equivariant GNNs
Haoyu Zhou, Ping Xue, Hao Zhang +1
Equivariant Graph Neural Networks (GNNs) are essential for physically consistent molecular simulations but suffer from high computational costs and memory bottlenecks, especially w…
RadDiff: Retrieval-Augmented Denoising Diffusion for Protein Inverse Folding
Jin Han, Tianfan Fu, Wu-Jun Li
Protein inverse folding, the design of an amino acid sequence based on a target protein structure, is a fundamental problem of computational protein engineering. Existing methods e…
Quantized SO(3)-Equivariant Graph Neural Networks for Efficient Molecular Property Prediction
Haoyu Zhou, Ping Xue, Hao Zhang +1
Deploying 3D graph neural networks (GNNs) that are equivariant to 3D rotations (the group SO(3)) on edge devices is challenging due to their high computational cost. This paper add…
Navigating heterogeneous protein landscapes through geometry-aware smoothing
Srinivas Anumasa, Barath Chandran, Tingting Chen +15
The evolutionary fitness landscape of biological molecules is extremely sparse and heterogeneous, with functional sequences forming isolated dense ``islands'' within a vast combina…
EnzyPGM: Pocket-conditioned Generative Model for Substrate-specific Enzyme Design
Zefeng Lin, Zhihang Zhang, Weirong Zhu +4
Designing enzymes with substrate-binding pockets is a critical challenge in protein engineering, as catalytic activity depends on the precise interaction between pockets and substr…