34 citations · 119 across the 17 of their papers we have counts for
6 papers · 1 filter
Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets
Chang Liu, Vivian Li, Linus Leong +3
Geometric Graph Neural Networks (GNNs) and Transformers have become state-of-the-art for learning from 3D protein structures. However, their reliance on message passing prevents th…
Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design
Junhua Chen, Simon Mathis, Charles Harris +2
Generative modeling techniques such as Diffusion and Flow Matching have achieved significant successes in generating designable and diverse protein backbones. However, many current…
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
Andrei Rekesh, Miruna Cretu, Dmytro Shevchuk +6
Synthesizability remains a critical bottleneck in generative molecular design. While recent advances have addressed synthesizability in 2D graphs, extending these constraints to 3D…
TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
Carlos Vonessen, Charles Harris, Miruna Cretu +1
State-of-the-art models for 3D molecular generation are based on significant inductive biases, SE(3), permutation equivariance to respect symmetry and graph message-passing network…
Diffusion-Free Graph Generation with Next-Scale Prediction
Samuel Belkadi, Steve Hong, Marian Chen +3
Autoregressive models excel in efficiency and plug directly into the transformer ecosystem, delivering robust generalization, predictable scalability, and seamless workflows such a…
Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs
Batu El, Deepro Choudhury, Pietro Liò +1
We introduce Attention Graphs, a new tool for mechanistic interpretability of Graph Neural Networks (GNNs) and Graph Transformers based on the mathematical equivalence between mess…