Emmanuel Bengio

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

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,

2024-09-25

NeurIPS.cc/2024/Conference (poster)

QGFN: Controllable Greediness with Action Values

Elaine Lau

Stephen Zhewen Lu

Ling Pan

Doina Precup

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,

2024-09-25

NeurIPS.cc/2024/Conference (poster)

GFlowNet Pretraining with Inexpensive Rewards

Mohit Pandey

Gopeshh Subbaraj

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.

2024-09-15

ArXiv (prépublication)

GFlowNet Pretraining with Inexpensive Rewards

Mohit Pandey

Gopeshh Subbaraj

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.

2024-09-15

ArXiv (prépublication)

QGFN: Controllable Greediness with Action Values

Elaine Lau

Stephen Zhewen Lu

Ling Pan

Doina Precup

Generative Flow Networks (GFlowNets; GFNs) are a family of reward/energy-based generative methods for combinatorial objects, capable of gene… (voir plus)rating diverse and high-utility samples. However, 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,

2024-06-17

ICML.cc/2024/Workshop/SPIGM (poster)

Random Policy Evaluation Uncovers Policies of Generative Flow Networks

Haoran He

Qingpeng Cai 0001

Ling Pan

2024-06-04

ArXiv (prépublication)

Random Policy Evaluation Uncovers Policies of Generative Flow Networks

Haoran He

Qingpeng Cai 0001

Ling Pan

The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sampl… (voir plus)e objects with probability proportional to an unnormalized reward function. GFlowNets share a strong connection with reinforcement learning (RL) that typically aims to maximize reward. A number of recent works explored connections between GFlowNets and maximum entropy (MaxEnt) RL, which incorporates entropy regularization into the standard RL objective. However, the relationship between GFlowNets and standard RL remains largely unexplored, despite the inherent similarities in their sequential decision-making nature. While GFlowNets can discover diverse solutions through specialized flow-matching objectives, connecting them to standard RL can simplify their implementation through well-established RL principles and also improve RL's capabilities in diverse solution discovery (a critical requirement in many real-world applications), and bridging this gap can further unlock the potential of both fields. In this paper, we bridge this gap by revealing a fundamental connection between GFlowNets and one of the most basic components of RL -- policy evaluation. Surprisingly, we find that the value function obtained from evaluating a uniform policy is closely associated with the flow functions in GFlowNets. Building upon these insights, we introduce a rectified random policy evaluation (RPE) algorithm, which achieves the same reward-matching effect as GFlowNets based on simply evaluating a fixed random policy, offering a new perspective. Empirical results across extensive benchmarks demonstrate that RPE achieves competitive results compared to previous approaches, shedding light on the previously overlooked connection between (non-MaxEnt) RL and GFlowNets.

2024-06-04

ArXiv (prépublication)

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

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

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,

2024-05-31

ArXiv (prépublication)

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

Siddarth Venkatraman

Moksh J. Jain

Luca Scimeca

Minsu Kim

Marcin Sendera

Mohsin Hasan

Luke Rowe

Sarthak Mittal

Pablo Lemos

Alexandre Adam

Jarrid Rector-Brooks

Glen Berseth

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,

2024-05-31

ArXiv (prépublication)

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

Maksym Korablyov

Cheng-Hao Liu

Moksh J. Jain

Almer M. van der Sloot

Eric Jolicoeur

Edward Ruediger

Andrei Cristian Nica

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor I Butoi

Jarrid Rector-Brooks

Simon R. Blackburn

Leo Feng

Hadi Nekoei

Sai Krishna Gottipati

Priyesh Vijayan

Prateek Gupta

Ladislav Rampášek … (voir 14 de plus)

Sasikanth Avancha

Pierre-Luc Bacon

William L. Hamilton

Brooks Paige

Sanchit Misra

Stanisław Jastrzębski

Bharat Kaul

Doina Precup

José Miguel Hernández-Lobato

Marwin Segler

Michael M. Bronstein

Anne Marinier

Mike Tyers

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.

2024-05-02

ArXiv (prépublication)

Learning to Scale Logits for Temperature-Conditional GFlowNets

Minsu Kim

Joohwan Ko

Dinghuai Zhang

Ling Pan

Taeyoung Yun

Woo Chang Kim

Jinkyoo Park

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

2024-05-01

ICML.cc/2024/Conference (poster)

Investigating Generalization Behaviours of Generative Flow Networks

Lazar Atanackovic

Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (voir plus)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.

2024-02-01

arXiv (publié)