Chenghao Liu

Soojung Yang

Sidney L Lisanza

Francesca-Zhoufan Li

Hannes Stärk

Jacob Gershon

Lauren Hong

Pranam Chatterjee

Tommi Jaakkola

Regina Barzilay

David Baker

Frances H. Arnold

Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing me… (voir plus)dical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.

2025-01-01

ICLR.cc/2025/Workshop_Proposals (publié)

openreview.net

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

2025-01-01

ICLR (publié)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (voir plus)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

2024-10-10

ArXiv (prépublication)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (voir plus)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

2024-10-10

ArXiv (prépublication)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (voir plus)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

2024-10-10

ArXiv (prépublication)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Zhangzhi Peng

Zachary Quinn

Cheng-Hao Liu

Michael M. Bronstein

Pranam Chatterjee

Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very buil… (voir plus)ding blocks of life in protein sequences. However, application domains need to exert control over the generated data by steering the generative process - typically via RLHF - to satisfy a specified property, reward, or affinity metric. In this paper, we study the problem of steering Masked Diffusion Models (MDMs), a recent class of discrete diffusion models that offer a compelling alternative to traditional autoregressive models. We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference by learning to sample from a target Bayesian posterior. Our DDPP framework leads to a family of three novel objectives that are all simulation-free, and thus scalable while applying to general non-differentiable reward functions. Empirically, we instantiate DDPP by steering MDMs to perform class-conditional pixel-level image modeling, RLHF-based alignment of MDMs using text-based rewards, and finetuning protein language models to generate more diverse secondary structures and shorter proteins. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.

2024-10-10

ArXiv (prépublication)

RGFN: Synthesizable Molecular Generation Using GFlowNets

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

2024-09-25

NeurIPS.cc/2024/Conference (poster)

openreview.net

RGFN: Synthesizable Molecular Generation Using GFlowNets

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (voir plus) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.

2024-06-01

ArXiv (prépublication)

RGFN: Synthesizable Molecular Generation Using GFlowNets

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (voir plus) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.

2024-06-01

ArXiv (prépublication)

RGFN: Synthesizable Molecular Generation Using GFlowNets

Andrei Rekesh

Dmytro Shevchuk

Almer M. van der Sloot

Cheng-Hao Liu

Mike Tyers

Robert A. Batey

Generative models hold great promise for small molecule discovery, significantly increasing the size of search space compared to traditional… (voir plus) in silico screening libraries. However, most existing machine learning methods for small molecule generation suffer from poor synthesizability of candidate compounds, making experimental validation difficult. In this paper we propose Reaction-GFlowNet (RGFN), an extension of the GFlowNet framework that operates directly in the space of chemical reactions, thereby allowing out-of-the-box synthesizability while maintaining comparable quality of generated candidates. We demonstrate that with the proposed set of reactions and building blocks, it is possible to obtain a search space of molecules orders of magnitude larger than existing screening libraries coupled with low cost of synthesis. We also show that the approach scales to very large fragment libraries, further increasing the number of potential molecules. We demonstrate the effectiveness of the proposed approach across a range of oracle models, including pretrained proxy models and GPU-accelerated docking.

2024-06-01

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

Emmanuel Bengio

Kostiantyn Lapchevskyi

Daniel St-Cyr

Doris Alexandra Schuetz

Victor I Butoi

Simon R. Blackburn

Leo Feng

Hadi Nekoei

Sai Krishna Gottipati

Priyesh Vijayan

Prateek Gupta

Ladislav Rampasek … (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)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Cheng-Hao Liu

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient---and no data samples---to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is *simulation-free*, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-05-01

ICML.cc/2024/Conference (poster)