Jungyoon Lee

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities

Valentin De Bortoli

Arnaud Doucet

Michael M. Bronstein

Alexander Tong

Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact sc… (voir plus)ientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita

2025-06-19

ArXiv (prépublication)

arxiv.org

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities

Tara Akhound-Sadegh

Jungyoon Lee

Joey Bose

Valentin De Bortoli

Arnaud Doucet

Michael M. Bronstein

Dominique Beaini

Siamak Ravanbakhsh

Kirill Neklyudov

Alexander Tong

Sampling efficiently from a target unnormalized probability density remains a core challenge, with relevance across countless high-impact sc… (voir plus)ientific applications. A promising approach towards this challenge is the design of amortized samplers that borrow key ideas, such as probability path design, from state-of-the-art generative diffusion models. However, all existing diffusion-based samplers remain unable to draw samples from distributions at the scale of even simple molecular systems. In this paper, we propose Progressive Inference-Time Annealing (PITA), a novel framework to learn diffusion-based samplers that combines two complementary interpolation techniques: I.) Annealing of the Boltzmann distribution and II.) Diffusion smoothing. PITA trains a sequence of diffusion models from high to low temperatures by sequentially training each model at progressively higher temperatures, leveraging engineered easy access to samples of the temperature-annealed target density. In the subsequent step, PITA enables simulating the trained diffusion model to procure training samples at a lower temperature for the next diffusion model through inference-time annealing using a novel Feynman-Kac PDE combined with Sequential Monte Carlo. Empirically, PITA enables, for the first time, equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates with dramatically lower energy function evaluations. Code available at: https://github.com/taraak/pita

2025-06-11

ICML.cc/2025/Workshop/GenBio (spotlight)

doi.org

openreview.net

Scaling Deep Learning Solutions for Transition Path Sampling

Jungyoon Lee

Michael Plainer

Yuanqi Du

Lars Holdijk

Rob Brekelmans

Carla P Gomes

Dominique Beaini

Kirill Neklyudov

Transition path sampling (TPS) is an important method for studying rare events, such as they happen in chemical reactions or protein folding… (voir plus). These events occur so infrequently that traditional simulations are often impractical, and even recent machine-learning approaches struggle to address this issue for larger systems. In this paper, we propose using modern deep learning techniques to improve the scalability of TPS methods significantly. We highlight the need for better evaluations in the existing literature and start by formulating TPS as a sampling problem over an unnormalized target density and introduce relevant evaluation metrics to assess the effectiveness of TPS solutions from this perspective. To develop a scalable approach, we explore several design choices, including a problem-informed neural network architecture, simulated annealing, the integration of prior knowledge into the sampling process, and attention mechanisms. Finally, we conduct a comprehensive empirical study and compare these design choices with other recently developed deep-learning methods for rare event sampling.

2025-03-05

ICLR.cc/2025/Workshop/GEM (publié)

openreview.net

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation

Majdi Hassan

Nikhil Shenoy

Jungyoon Lee

Hannes Stärk

Stephan Thaler

Dominique Beaini

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery.… (voir plus) Existing state- of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to gen- erate initial structures and diffuse over torsion angles. In this work, we introduce Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed flow matching approach with equivariance and harmonic prior alleviates the need for complex internal geometry calculations and large architectures, contrary to the prevailing methods in the field. Our approach results in a straightforward and scalable method that directly operates on all-atom coordinates with minimal assumptions. With the advantages of equivariance and flow matching, ET-Flow significantly increases the precision and physical validity of the generated con- formers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

openreview.net