arXiv Machine Learning By Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu, Pheng Ann Heng, Tianfan Fu

RiboSphere: Learning Unified and Efficient Representations of RNA Structures

Read the original on arXiv Machine Learning →

arXiv:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.

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arXiv Machine Learning
Jul 15

SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems

arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.

By Yuxuan Ren, Fan Yang, Jianhua Yao, Yatao Bian
arXiv Machine Learning
Jun 16

Learning Topological Representations for Molecular Dynamics

arXiv:2606. 14737v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulations generate trajectories in a high-dimensional configuration space whose analysis critically depends on molecular descriptors, typically handcrafted observables or learned kinetic embeddings.

By Dominik Geng, Florian Graf, Martin Uray, Roland Kwitt
arXiv Machine Learning
Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

arXiv:2607. 05736v1 Announce Type: new Abstract: Molecular property prediction often relies on isolated data modalities, where continuous 3D graph neural networks (GNNs) struggle to efficiently capture long-range topological dependencies and exact macroscopic heuristics.

By Qiwei Han, Chi Zhou, Ruobing Wang, Zheng Ma