427 citations · 804 across the 14 of their papers we have counts for
15 papers
A Scientific Reasoning Model for Organic Synthesis Procedure Generation
Guoqing Liu, Junren Li, Zihan Zhao +7
Solving computer-aided synthesis planning is essential for enabling fully automated, robot-assisted synthesis workflows and improving the efficiency of drug discovery. A key challe…
Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space
Sebastian Ehlert, Jan Hermann, Thijs Vogels +11
Accurate thermochemical data with sub-chemical accuracy (within 1 kcal mol of the empirical ground truth) are essential for advancing computational chemistry methods. Howeve…
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang +6
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accur…
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Ilia Igashov, Hannes Stärk, Clément Vignac +5
Fragment-based drug discovery has been an effective paradigm in early-stage drug development. An open challenge in this area is designing linkers between disconnected molecular fra…
Multivariate Time Series Forecasting with Latent Graph Inference
Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrat…
Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac +1
This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise…
A Study of Joint Graph Inference and Forecasting
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras +3
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there…
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs +2
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph…
E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras, Emiel Hoogeboom, Max Welling
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks…
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak +1
This paper introduces a new method to build linear flows, by taking the exponential of a linear transformation. This linear transformation does not need to be invertible itself, an…
Neural Enhanced Belief Propagation on Factor Graphs
Victor Garcia Satorras, Max Welling
A graphical model is a structured representation of locally dependent random variables. A traditional method to reason over these random variables is to perform inference using bel…
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical mo…
Few-Shot Learning with Graph Neural Networks
Victor Garcia, Joan Bruna
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label…