210 citations · 727 across the 35 of their papers we have counts for
10 papers · 1 filter
Equivariant Flow Matching with Hybrid Probability Transport
Yuxuan Song, Jingjing Gong, Minkai Xu +5
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially…
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Yiming Wang, Yuxuan Song, Yiqun Wang +4
Retrosynthesis poses a key challenge in biopharmaceuticals, aiding chemists in finding appropriate reactant molecules for given product molecules. With reactants and products repre…
Scaling Riemannian Diffusion Models
Aaron Lou, Minkai Xu, Stefano Ermon
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric com…
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Ling Yang, Ye Tian, Minkai Xu +7
GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output re…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Bo Qiang, Yuxuan Song, Minkai Xu +5
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate m…
Geometric Latent Diffusion Models for 3D Molecule Generation
Minkai Xu, Alexander Powers, Ron Dror +2
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as mol…
MADiff: Offline Multi-agent Learning with Diffusion Models
Zhengbang Zhu, Minghuan Liu, Liyuan Mao +5
Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle wit…
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua, Sitao Luan, Minkai Xu +4
Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing me…
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Sitao Luan, Chenqing Hua, Minkai Xu +6
Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Netwo…