3 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
HamFormer: A Hamiltonian Flow-based SE(3)-Transformer for Conformation Optimization
Minjie Cheng, Zhenghao Liu, Yitian Wang +3
The Transformer architecture plays a central role in modern deep learning, and its SE(3)-equivariant variants have been widely applied to molecular conformation modeling and optimi…
DC-Flow: Target-Conditioned Cyclic Peptide Design via Delayed-Coupling Flow Matching
Yitian Wang, Minjie Cheng, Yaning Cui +3
Target-conditioned cyclic peptide design requires coordinating interfacial side chains with macrocyclic closure. Existing end-to-end generators concurrently update geometric and ch…
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction
Fanmeng Wang, Wentao Guo, Minjie Cheng +3
Polymers are high-molecular-weight compounds constructed by the covalent bonding of numerous identical or similar monomers so that their 3D structures are complex yet exhibit unign…
A Quasi-Wasserstein Loss for Learning Graph Neural Networks
Minjie Cheng, Hongteng Xu
When learning graph neural networks (GNNs) in node-level prediction tasks, most existing loss functions are applied for each node independently, even if node embeddings and their l…
Regularized Optimal Transport Layers for Generalized Global Pooling Operations
Hongteng Xu, Minjie Cheng
Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) re…
Revisiting Global Pooling through the Lens of Optimal Transport
Minjie Cheng, Hongteng Xu
Global pooling is one of the most significant operations in many machine learning models and tasks, whose implementation, however, is often empirical in practice. In this study, we…