6 papers
Approximate Equivariance via Projection-based Regularisation
Torben Berndt, Jan Stühmer
Equivariance is a powerful inductive bias in neural networks, improving generalisation and physical consistency. Recently, however, non-equivariant models have regained attention,…
Learning conformational ensembles of proteins based on backbone geometry
Nicolas Wolf, Leif Seute, Vsevolod Viliuga +3
Deep generative models have recently been proposed for sampling protein conformations from the Boltzmann distribution, as an alternative to often prohibitively expensive Molecular…
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
Torben Berndt, Benjamin Walker, Tiexin Qin +2
Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential…
Flexibility-Conditioned Protein Structure Design with Flow Matching
Vsevolod Viliuga, Leif Seute, Nicolas Wolf +4
Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-…
Set-LLM: A Permutation-Invariant LLM
Beni Egressy, Jan Stühmer
While large language models (LLMs) demonstrate impressive capabilities across numerous applications, their robustness remains a critical concern. This paper is motivated by a speci…
Generating Highly Designable Proteins with Geometric Algebra Flow Matching
Simon Wagner, Leif Seute, Vsevolod Viliuga +3
We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA),…