NeurIPS 2025

Learning conformational ensembles of proteins based
on backbone geometry
Nicolas Wolf ∗1,2,3
Leif Seute ∗2,1,3
Vsevolod Viliuga 4,1
Simon Wagner 3
Jan Stühmer 2,5
Frauke Gräter 1,2,3
1Max Planck Institute for Polymer Research, Mainz, Germany
2Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
3IWR, Heidelberg University, Heidelberg, Germany
4SciLifeLab and DBB at Stockholm University, Stockholm, Sweden
5IAR, Karlsruhe Institute of Technology, Karlsruhe, Germany
Abstract
Deep generative models have recently been proposed for sampling protein confor-
mations from the Boltzmann distribution, as an alternative to often prohibitively
expensive Molecular Dynamics simulations. However, current state-of-the-art ap-
proaches rely on fine-tuning pre-trained folding models and evolutionary sequence
information, limiting their applicability and efficiency, and introducing potential
biases. In this work, we propose a flow matching model for sampling protein
conformations based solely on backbone geometry – BBFlow. We introduce a
geometric encoding of the backbone equilibrium structure as input and propose to
condition not only the flow but also the prior distribution on the respective equilib-
rium structure, eliminating the need for evolutionary information. The resulting
model is orders of magnitudes faster than current state-of-the-art approaches at
comparable accuracy, is transferable to multi-chain proteins, and can be trained
from scratch in a few GPU days. In our experiments, we demonstrate that the
proposed model achieves competitive performance with reduced inference time,
across not only an established benchmark of naturally occurring proteins but also
de novo proteins, for which evolutionary information is scarce or absent. BBFlow
is available at https://github.com/graeter-group/bbflow.
1
Introduction
In recent years, the field of protein structure prediction has been revolutionized by geometric deep
learning [19, 4, 27]. Jumper et al. [19] introduced A...