5 citations · 9 across the 6 of their papers we have counts for
10 papers
Riemannian Consistency Model
Chaoran Cheng, Yusong Wang, Yuxin Chen +3
Consistency models are a class of generative models that enable few-step generation for diffusion and flow matching models. While consistency models have achieved promising results…
MCGM: Multi-stage Clustered Global Modeling for Long-range Interactions in Molecules
Haodong Pan, Yusong Wang, Nanning Zheng +1
Geometric graph neural networks (GNNs) excel at capturing molecular geometry, yet their locality-biased message passing hampers the modeling of long-range interactions. Current sol…
Pretraining a Foundation Model for Small-Molecule Natural Products
Yuheng Ding, Bo Qiang, Shaoning Li +8
Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep lea…
Neural PM: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
Yusong Wang, Chaoran Cheng, Shaoning Li +5
Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range inter…
Improving AlphaFlow for Efficient Protein Ensembles Generation
Shaoning Li, Mingyu Li, Yusong Wang +4
Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generat…
Flow: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Shaoning Li, Yusong Wang, Mingyu Li +4
Molecular dynamics (MD) is a crucial technique for simulating biological systems, enabling the exploration of their dynamic nature and fostering an understanding of their functions…