Emmanuel Bengio

AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms

Emmanuel Bengio

Sanjeev Raja

Yui Tik Pang

Kerstin Klaeser

Cristian Gabellini

Nikhil Shenoy

Francesco Di Giovanni

Prudencio Tossou

We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configu… (see more)rations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD). This is in contrast with existing generative models that remove degrees of freedom by operating on coarse-grained, vacuum, or implicit solvent systems. Operating at this resolution allows for post-processing through force field energy evaluations and MD simulations, and enables the prediction of relevant properties in a gray-box manner (as ensemble averages of potential energy evaluations over generated samples). We demonstrate the utility of this paradigm on absolute hydration free energy (AHFE), producing estimates 4-10x faster and with comparable accuracy to standard GPU-based MD. By generating uncorrelated samples from alchemical Boltzmann distributions, we create more accurate, interpretable, and refinable ensemble predictions with calibrated uncertainty estimates, unlike regression methods which are entirely black-box predictors. Our approach also yields predictable benefits from increasing train- and test-time compute, realized by scaling model size and generating more samples, respectively. We believe that this approach demonstrates the utility of high-resolution ensemble generation for free energy estimation, with future potential to replace MD in tasks such as the prediction of lipophilicity, membrane permeability, or absolute binding free energy (ABFE) -- whose grounding and interpretability may be critical for the development of new drugs and materials.

2026-07-02

arXiv (preprint)

doi.org

arxiv.org

Improved Off-policy Reinforcement Learning in Biological Sequence Design

Alex Hernández-García

Jinkyoo Park

Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although rei… (see more)nforcement learning methods use proxy models for rapid reward evaluation, insufficient training data can cause proxy misspecification on out-of-distribution inputs. To address this, we propose a novel off-policy search,

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

doi.org

proceedings.mlr.press

Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation

Mohit Pandey

Gopeshh Subbaraj

Artem Cherkasov

Martin Ester

Emmanuel Bengio

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

doi.org

proceedings.mlr.press

Random Policy Evaluation Uncovers Policies of Generative Flow Networks

Haoran He

Emmanuel Bengio

Qingpeng Cai 0001

Ling Pan

2025-10-05

Proceedings of the 42nd International Conference on Machine Learning (published)

proceedings.mlr.press

Torsional-GFN: a conditional conformation generator for small molecules

Lena Nehale Ezzine

Alex Hernández-García

2025-06-10

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

doi.org

openreview.net

Virtual Cells: Predict, Explain, Discover

Emmanuel Noutahi

Jason Hartford

Prudencio Tossou

Shawn Whitfield

Ali Denton

Cas Wognum

Kristina Ulicna

Michael Craig

Jonathan Hsu

Michael Cuccarese

Emmanuel Bengio

Dominique Beaini

Christopher Gibson

Daniel Cohen

Berton Earnshaw

2025-05-19

ArXiv (preprint)

doi.org

arxiv.org

Adaptive Teachers for Amortized Samplers

Sungsoo Ahn

Jinkyoo Park

Nikolay Malkin

Yoshua Bengio

Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnorma… (see more)lized density where exact sampling is intractable. When sampling is implemented as a sequential decision-making process, reinforcement learning (RL) methods, such as generative flow networks, can be used to train the sampling policy. Off-policy RL training facilitates the discovery of diverse, high-reward candidates, but existing methods still face challenges in efficient exploration. We propose to use an adaptive training distribution (the \teacher) to guide the training of the primary amortized sampler (the \student). The \teacher, an auxiliary behavior model, is trained to sample high-loss regions of the \student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum. We validate the effectiveness of this approach in a synthetic environment designed to present an exploration challenge, two diffusion-based sampling tasks, and four biochemical discovery tasks demonstrating its ability to improve sample efficiency and mode coverage. Source code is available at https://github.com/alstn12088/adaptive-teacher.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

doi.org

openreview.net

Towards Improving Exploration Through Sibling Augmented GFlowNets

2025-04-22

International Conference on Learning Representations (Accept (Poster))

openreview.net

Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL

Laurence Perreault-Levasseur

Yoshua Bengio

Glen Berseth

Nikolay Malkin

This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (R… (see more)L) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.

2025-03-05

ICLR.cc/2025/Workshop/DeLTa (poster)

doi.org

openreview.net

Action Abstractions for Amortized Sampling

Lena Nehale Ezzine

Nikolay Malkin

As trajectories sampled by policies used by reinforcement learning (RL) and generative flow networks (GFlowNets) grow longer, credit assignm… (see more)ent and exploration become more challenging, and the long planning horizon hinders mode discovery and generalization. The challenge is particularly pronounced in entropy-seeking RL methods, such as generative flow networks, where the agent must learn to sample from a structured distribution and discover multiple high-reward states, each of which take many steps to reach. To tackle this challenge, we propose an approach to incorporate the discovery of action abstractions, or high-level actions, into the policy optimization process. Our approach involves iteratively extracting action subsequences commonly used across many high-reward trajectories and `chunking' them into a single action that is added to the action space. In empirical evaluation on synthetic and real-world environments, our approach demonstrates improved sample efficiency performance in discovering diverse high-reward objects, especially on harder exploration problems. We also observe that the abstracted high-order actions are interpretable, capturing the latent structure of the reward landscape of the action space. This work provides a cognitively motivated approach to action abstraction in RL and is the first demonstration of hierarchical planning in amortized sequential sampling.

2025-01-21

International Conference on Learning Representations (poster)

doi.org

openreview.net

Investigating Generalization Behaviours of Generative Flow Networks

Lazar Atanackovic

Emmanuel Bengio

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (see more)ces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, we find that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. We also find that GFlowNets are sensitive to being trained offline and off-policy; however, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.

2024-12-31

Trans. Mach. Learn. Res. (published)

doi.org

arxiv.org

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints

M. Cretu

Charles Harris

Julien Roy

Emmanuel Bengio

Pietro Lio’

Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular m… (see more)otifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reactions and buyable reactants to sequentially build new molecules. By incorporating forward synthesis as an explicit constraint of the generative mechanism, we aim at bridging the gap between in silico molecular generation and real world synthesis capabilities. We evaluate our approach using synthetic accessibility scores and an independent retrosynthesis tool to assess the synthesizability of our compounds, and motivate the choice of GFlowNets through considerable improvement in sample diversity compared to baselines. Additionally, we identify challenges with reaction encodings that can complicate traversal of the MDP in the backward direction. To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework. This approach enables our model to successfully identify synthesis pathways for previously unseen molecules.

2024-12-31

ICLR (published)

doi.org

arxiv.org

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Emmanuel Bengio

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