Papers
1–4 of 4Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equivariance through arch...
Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction
Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key challenge arises in...
Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding
Molecular understanding is central to advancing areas such as scientific discovery, yet Large Language Models (LLMs) struggle to understand molecular graphs effectively. Existing graph-LLM bridges oft...
MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields
Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angular expansions, limit...