Emmanuel Bengio

QGFN: Controllable Greediness with Action Values

Stephen Zhewen Lu

Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating … (voir plus)diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate,

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

doi.org

openreview.net

Amortizing intractable inference in diffusion models for vision, language, and control

Nikolay Malkin

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors … (voir plus)in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data,

2024-12-09

Advances in Neural Information Processing Systems (publié)

doi.org

openreview.net

Efficient Biological Data Acquisition through Inference Set Design

Ihor Neporozhnii

Julien Roy

Emmanuel Bengio

Jason Hartford

In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs… (voir plus). These experiments are expensive, so one might hope to reduce their cost by only experimenting on a subset of the compounds, and predicting the outcomes of the remaining experiments. In this work, we model this scenario as a sequential subset selection problem: we aim to select the smallest set of candidates in order to achieve some desired level of accuracy for the system as a whole. Our key observation is that, if there is heterogeneity in the difficulty of the prediction problem across the input space, selectively obtaining the labels for the hardest examples in the acquisition pool will leave only the relatively easy examples to remain in the inference set, leading to better overall system performance. We call this mechanism inference set design, and propose the use of a confidence-based active learning solution to prune out these challenging examples. Our algorithm includes an explicit stopping criterion that interrupts the acquisition loop when it is sufficiently confident that the system has reached the target performance. Our empirical studies on image and molecular datasets, as well as a real-world large-scale biological assay, show that active learning for inference set design leads to significant reduction in experimental cost while retaining high system performance.

2024-10-24

ArXiv (prépublication)

doi.org

arxiv.org

GFlowNet Pretraining with Inexpensive Rewards

Mohit Pandey

Gopeshh Subbaraj

Emmanuel Bengio

Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and … (voir plus)high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.

2024-09-14

ArXiv (prépublication)

doi.org

arxiv.org

Learning to Scale Logits for Temperature-Conditional GFlowNets

Joohwan Ko

Woo Chang Kim

Jinkyoo Park

Emmanuel Bengio

Yoshua Bengio

GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, tempera… (voir plus)ture-conditional GFlowNets can introduce temperature-based controllability for exploration and exploitation. We propose \textit{Logit-scaling GFlowNets} (Logit-GFN), a novel architectural design that greatly accelerates the training of temperature-conditional GFlowNets. It is based on the idea that previously proposed approaches introduced numerical challenges in the deep network training, since different temperatures may give rise to very different gradient profiles as well as magnitudes of the policy's logits. We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly. Also, using Logit-GFN, GFlowNets can be improved by having better generalization capabilities in offline learning and mode discovery capabilities in online learning, which is empirically verified in various biological and chemical tasks. Our code is available at https://github.com/dbsxodud-11/logit-gfn

2024-07-22

International Conference on Machine Learning (Accept (Poster))

doi.org

proceedings.mlr.press

Local Search GFlowNets

Sungsoo Ahn

Jinkyoo Park

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.

2024-05-06

International Conference on Learning Representations (Accept (spotlight))

doi.org

openreview.net

Maximum entropy GFlowNets with soft Q-learning

Sobhan Mohammadpour

Emmanuel Bengio

Emma Frejinger

Pierre-Luc Bacon

Generative Flow Networks (GFNs) have emerged as a powerful tool for sampling discrete objects from unnormalized distributions, offering a sc… (voir plus)alable alternative to Markov Chain Monte Carlo (MCMC) methods. While GFNs draw inspiration from maximum entropy reinforcement learning (RL), the connection between the two has largely been unclear and seemingly applicable only in specific cases. This paper addresses the connection by constructing an appropriate reward function, thereby establishing an exact relationship between GFNs and maximum entropy RL. This construction allows us to introduce maximum entropy GFNs, which, in contrast to GFNs with uniform backward policy, achieve the maximum entropy attainable by GFNs without constraints on the state space.

2024-05-01

International Conference on Artificial Intelligence and Statistics (Accept (Poster))

doi.org

proceedings.mlr.press

Generative Active Learning for the Search of Small-Molecule Protein Binders

Maksym Korablyov

Cheng-Hao Liu

Moksh Jain

Almer Van Der Sloot

Éric Jolicoeur

Edward Ruediger

Andrei Nica

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor Ion Butoi

Saikrishna Gottipati

Prateek Gupta

Ladislav Rampasek … (voir 14 de plus)

Sasikanth Avancha

Pierre-Luc Bacon

William Hamilton

Brooks Paige

Sanchit Misra

Stanislaw Jastrzebski

Bharat Kaul

Doina Precup

José Miguel Hernández-Lobato

Marwin Segler

Michael Bronstein

Anne Marinier

Mike Tyers

Yoshua Bengio

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

2023-12-31

arXiv (prépublication)

doi.org

arxiv.org

A community effort in SARS-CoV-2 drug discovery.

Johannes Schimunek

Philipp Seidl

Katarina Elez

Tim Hempel

Tuan Le

Frank Noé

Simon Olsson

Lluís Raich

Robin Winter

Hatice Gokcan

Filipp Gusev

Evgeny M. Gutkin

Olexandr Isayev

Maria G. Kurnikova

Chamali H. Narangoda

Roman Zubatyuk

Ivan P. Bosko

Konstantin V. Furs

Anna D. Karpenko

Yury V. Kornoushenko … (voir 133 de plus)

Mikita Shuldau

Artsemi Yushkevich

Mohammed B. Benabderrahmane

Patrick Bousquet‐Melou

Ronan Bureau

Beatrice Charton

Bertrand C. Cirou

Gérard Gil

William J. Allen

Suman Sirimulla

Stanley Watowich

Nick Antonopoulos

Nikolaos Epitropakis

Agamemnon Krasoulis

Vassilis Pitsikalis

Stavros Theodorakis

Igor Kozlovskii

Anton Maliutin

Alexander Medvedev

Petr Popov

Mark Zaretckii

Hamid Eghbal‐Zadeh

Christina Halmich

Sepp Hochreiter

Andreas Mayr

Peter Ruch

Michael Widrich

Francois Berenger

Ashutosh Kumar

Yoshihiro Yamanishi

Kam Y. J. Zhang

Emmanuel Bengio

Yoshua Bengio

Moksh J. Jain

Maksym Korablyov

Cheng-Hao Liu

Gilles Marcou

M. Gilles

Enrico Glaab

Kelly Barnsley

Suhasini M. Iyengar

Mary Jo Ondrechen

V. Joachim Haupt

Florian Kaiser

Michael Schroeder

Luisa Pugliese

Simone Albani

Christina Athanasiou

Andrea Beccari

Paolo Carloni

Giulia D'Arrigo

Eleonora Gianquinto

Jonas Goßen

Anton Hanke

Benjamin P. Joseph

Daria B. Kokh

Sandra Kovachka

Candida Manelfi

Goutam Mukherjee

Abraham Muñiz‐Chicharro

Francesco Musiani

Ariane Nunes‐Alves

Giulia Paiardi

Giulia Rossetti

S. Kashif Sadiq

Francesca Spyrakis

Carmine Talarico

Alexandros Tsengenes

Rebecca C. Wade

Conner Copeland

Jeremiah Gaiser

Daniel R. Olson

Amitava Roy

Vishwesh Venkatraman

Travis J. Wheeler

Haribabu Arthanari

Klara Blaschitz

Marco Cespugli

Vedat Durmaz

Konstantin Fackeldey

Patrick D. Fischer

Christoph Gorgulla

Christian Gruber

Karl Gruber

Michael Hetmann

Jamie E. Kinney

Krishna M. Padmanabha Das

Shreya Pandita

Amit Singh

Georg Steinkellner

Guilhem Tesseyre

Gerhard Wagner

Zi‐Fu Wang

Ryan J. Yust

Dmitry S. Druzhilovskiy

Dmitry A. Filimonov

Pavel V. Pogodin

Vladimir Poroikov

Anastassia V. Rudik

Leonid A. Stolbov

Alexander V. Veselovsky

Maria De Rosa

Giada De Simone

Maria R. Gulotta

Jessica Lombino

Nedra Mekni

Ugo Perricone

Arturo Casini

Amanda Embree

D. Benjamin Gordon

David Lei

Katelin Pratt

Christopher A. Voigt

Kuang‐Yu Chen

Yves Jacob

Tim Krischuns

Pierre Lafaye

Agnès Zettor

M. Luis Rodríguez

Kris M. White

Daren Fearon

Frank Von Delft

Martin A. Walsh

Dragos Horvath

Charles L. Brooks

Babak Falsafi

Bryan Ford

Adolfo García‐Sastre

Sang Yup Lee

Nadia Naffakh

Alexandre Varnek

Günter Klambauer

Thomas M. Hermans

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availabili… (voir plus)ty of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

2023-11-13

Molecular Informatics (publié)

doi.org

Baking Symmetry into GFlowNets

George Ma

Emmanuel Bengio

Yoshua Bengio

Dinghuai Zhang

GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects increment… (voir plus)ally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient and potentially incorrect flow functions. As a consequence, the reward and diversity of the generated objects decrease. In this study, our objective is to integrate symmetries into GFlowNets by identifying equivalent actions during the generation process. Experimental results using synthetic data demonstrate the promising performance of our proposed approaches.

2023-10-27

AI4Science @ Neural Information Processing Systems (présentation orale)

doi.org

openreview.net

DGFN: Double Generative Flow Networks

2023-10-26

NeurIPS.cc/2023/Workshop/GenBio (poster)

doi.org

openreview.net

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability

Andrei Nica

Tom Bosc

Yoshua Bengio

Nikolay Malkin

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized … (voir plus)target density and have been successfully used for various probabilistic modeling tasks. Existing training objectives for GFlowNets are either local to states or transitions, or propagate a reward signal over an entire sampling trajectory. We argue that these alternatives represent opposite ends of a gradient bias-variance tradeoff and propose a way to exploit this tradeoff to mitigate its harmful effects. Inspired by the TD(

2023-07-24

International Conference on Machine Learning (Accept (Oral & Poster))

doi.org

proceedings.mlr.press

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Emmanuel Bengio

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La plateforme Mila Ventures

Boussole des politiques en IA

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Emmanuel Bengio

Biographie

Publications