Chenghao Liu

OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction

Emily Jin

Andrei Cristian Nica

Mikhail Galkin

Kin Long Kelvin Lee

Santiago Miret

Frances H. Arnold

Michael M. Bronstein

Avishek Bose

Alexander Tong

Cheng-Hao Liu

2025-11-30

arXiv (publié)

arxiv.org

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Mohsin Hasan

Zhangzhi Peng

Zachary Quinn

Michael Bronstein

Pranam Chatterjee

Avishek Joey Bose

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.

2025-04-22

International Conference on Learning Representations (Accept (Poster))

Integrating Generative and Experimental Platforms for Biomolecular Design

Cheng-Hao 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.

2024-12-31

ICLR.cc/2025/Workshop_Proposals (publié)

RGFN: Synthesizable Molecular Generation Using GFlowNets

Michał Koziarski

Andrei Rekesh

Dmytro Shevchuk

Almer Van Der Sloot

Piotr Gainski

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.

2024-09-24

NeurIPS.cc/2024/Conference (poster)

Multi-Fidelity Active Learning with GFlowNets

Alex Hernandez-Garcia

Nikita Saxena

Moksh J. Jain

Cheng-Hao Liu

In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanw… (voir plus)hile, the progress in machine learning has turned it into a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, high-dimensional spaces, where querying a high fidelity, black-box objective function is very expensive. Progress in machine learning methods that can efficiently tackle such problems would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose the use of GFlowNets for multi-fidelity active learning, where multiple approximations of the black-box function are available at lower fidelity and cost. GFlowNets are recently proposed methods for amortised probabilistic inference that have proven efficient for exploring large, high-dimensional spaces and can hence be practical in the multi-fidelity setting too. Here, we describe our algorithm for multi-fidelity active learning with GFlowNets and evaluate its performance in both well-studied synthetic tasks and practically relevant applications of molecular discovery. Our results show that multi-fidelity active learning with GFlowNets can efficiently leverage the availability of multiple oracles with different costs and fidelities to accelerate scientific discovery and engineering design.

2024-07-24

TMLR (accepté)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Avishek Joey Bose

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

2024-04-30

ICML.cc/2024/Conference (poster)

proceedings.mlr.press

Integrating Generative and Experimental Platforms or Biomolecular Design

Cheng-Hao Liu

Jason Yim

Soojung Yang

Sidney Lisanza

Francesca-Zhoufan Li

Pranam Chatterjee

Tommi Jaakkola

Regina Barzilay

David Baker

Frances H. Arnold

2024-03-07

ICLR.cc/2024/Workshop_Proposals (publié)

Diffusion Generative Flow Samplers: Improving Learning Signals Through Partial Trajectory Optimization

Dinghuai Zhang

Ricky T. Q. Chen

Cheng-Hao Liu

Aaron Courville

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine lear… (voir plus)ning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional "flow function". Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.

2024-01-15

ICLR.cc/2024/Conference (poster)

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

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)

arxiv.org

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation

Guillaume Huguet

James Vuckovic

Kilian Fatras

Éric Thibodeau-Laufer

Cheng-Hao Liu

Michael Bronstein

Avishek Joey Bose

2023-12-31

Advances in Neural Information Processing Systems 37 (publié)

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

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)