41 citations · 73 across the 11 of their papers we have counts for
11 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…
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
He Zhang, Chang Liu, Zun Wang +5
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +8
This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constr…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +1
This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values…
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
He Zhang, Siyuan Liu, Jiacheng You +6
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for…
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du +4
Group equivariance (e.g. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology. It enables robust and accurate…