29 citations · 31 across the 6 of their papers we have counts for
7 papers
Learning consistent molecular mechanics force fields from first principles
Berkay Günes, Leif Seute, Jigyasa Nigam +1
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy. They decompose tot…
Predicting directional flexibility in proteins
Vsevolod Viliuga, Leif Seute, Matteo Tadiello +3
Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge move…
Universal Time Series Generation with Neural Controlled Differential Equations
Torben Berndt, Elyes Farjallah, Leif Seute +3
Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time-series modelling. While the…
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-…
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…
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),…
Grappa -- A Machine Learned Molecular Mechanics Force Field
Leif Seute, Eric Hartmann, Jan Stühmer +1
Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted th…