1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Energy-Based Flow Matching for Generating 3D Molecular Structure
Wenyin Zhou, Christopher Iliffe Sprague, Vsevolod Viliuga +3
Molecular structure generation is a fundamental problem that involves determining the 3D positions of molecules' constituents. It has crucial biological applications, such as molec…
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-…
Hessian-Informed Flow Matching
Christopher Iliffe Sprague, Arne Elofsson, Hossein Azizpour
Modeling complex systems that evolve toward equilibrium distributions is important in various physical applications, including molecular dynamics and robotic control. These systems…
Decoding complexity: how machine learning is redefining scientific discovery
Ricardo Vinuesa, Paola Cinnella, Jean Rabault +10
As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an ess…
Stable Autonomous Flow Matching
Christopher Iliffe Sprague, Arne Elofsson, Hossein Azizpour
In contexts where data samples represent a physically stable state, it is often assumed that the data points represent the local minima of an energy landscape. In control theory, i…