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20242026
most citedConstraint Decoupled Latent Diffusion for Protein Backmapping

5 citations · 6 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2026

OctMole: Multiscale 3D Molecular Generation with Hierarchical Spatial Flows

Yu-Tong Jin, Yi-Yang Tian, Qiwei Ye +1

Generating accurate 3D molecules requires jointly determining a variable number of atoms, their identities, and geometry across spatial scales: global shape emerges coarsely, where…

cs.LG2026

Towards Unified Multi-task EEG Analysis with Low-Rank Adaptation

Sicheng Dai, Kai Chen, Hongwang Xiao +2

Recent self-supervised pre-training methods for electroencephalogram (EEG) have shown promising results. However, the pre-trained models typically require full fine-tuning on each…

cs.LG2026

Autoregressive Visual Decoding from EEG Signals

Sicheng Dai, Hongwang Xiao, Shan Yu +1

Electroencephalogram (EEG) signals have become a popular medium for decoding visual information due to their cost-effectiveness and high temporal resolution. However, current appro…

cs.LG2025

EPO: Diverse and Realistic Protein Ensemble Generation via Energy Preference Optimization

Yuancheng Sun, Yuxuan Ren, Zhaoming Chen +3

Accurate exploration of protein conformational ensembles is essential for uncovering function but remains hard because molecular-dynamics (MD) simulations suffer from high computat…

q-bio.BM2025

STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding

Hongwang Xiao, Wenjun Lin, Xi Chen +7

Understanding the intricate interplay among sequence, structure, and function remains a fundamental challenge in proteomics. The sequence-structure-function paradigm posits that bi…

cs.LG2024★ 5 cited

Constraint Decoupled Latent Diffusion for Protein Backmapping

Xu Han, Yuancheng Sun, Kai Chen +3

Coarse-grained (CG) molecular dynamics simulations enable efficient exploration of protein conformational ensembles. However, reconstructing atomic details from CG structures (back…

cs.LG2024★ 1 cited

Pre-training with Fractional Denoising to Enhance Molecular Property Prediction

Yuyan Ni, Shikun Feng, Xin Hong +5

Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, v…