6 citations · 6 across the 4 of their papers we have counts for
4 papers
Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
Kaipeng Zeng, Wenxi Zhai, Shengrui Xu +5
Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding…
MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation
Nianzu Yang, Kaipeng Zeng, Haotian Lu +7
Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational a…
Supercharging Graph Transformers with Advective Diffusion
Qitian Wu, Chenxiao Yang, Kaipeng Zeng +1
The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structure…
Molecule Generation for Drug Design: a Graph Learning Perspective
Nianzu Yang, Huaijin Wu, Kaipeng Zeng +2
Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm…