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Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences.
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
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The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service
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Exact stochastic simulation of coupled chemical reactions
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Markov Chains
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Approximate accelerated stochastic simulation of chemically reacting systems
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Lead- and drug-like compounds: the rule-of-five revolution
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A modified next reaction method for simulating chemical systems with time dependent propensities and delays
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Some extensions of score matching
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RDKit: Open-Source Cheminformatics Software, 2010
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Extended-Connectivity Fingerprints
David Rogers and Mathew Hahn · 2010
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ChEMBL: a large-scale bioactivity database for drug discovery
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik · 2018
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Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Hase, AkshatKumar Nigam, Pascal Friederich, and Alán Aspuru-Guzik · 2019
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Neural Empirical Bayes
Saeed Saremi and Aapo Hyvärinen · 2019
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Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Oops I Took a Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison · 2021
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Classifier-Free Diffusion Guidance
Jonathan Ho and Tim Salimans · 2021
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QMugs, quantum mechanical properties of drug-like molecules
Clemens Isert, Kenneth Atz, José Jiménez-Luna, and Gisbert Schneider · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Score-based Generative Modeling through Stochastic Differential Equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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FUDGE: Controlled Text Generation With Future Discriminators
Kevin Yang and Dan Klein · 2021
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A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
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Robust deep learning–based protein sequence design using ProteinMPNN
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, et al · 2022
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Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le · 2023
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
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Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
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Sparse Training of Discrete Diffusion Models for Graph Generation
Yiming Qin, Clément Vignac, and Pascal Frossard · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Equivariant Diffusion for Molecule Generation in 3D
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives · 2022
Cited alongside, same era.
Decoding gene regulation in the fly brain
Jasper Janssens, Sara Aibar, Ibrahim Ihsan Taskiran, Joy N Ismail, Alicia Estacio Gomez, Gabriel Aughey, Katina I Spanier, Florian V De Rop, Carmen Bravo Gonzalez-Blas, Marc Dionne, et al · 2022
Cited alongside, same era.
Diffusion-LM Improves Controllable Text Generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
Cited alongside, same era.
Concrete Score Matching: Generalized Score Matching for Discrete Data
Chenlin Meng, Kristy Choi, Jiaming Song, and Stefano Ermon · 2022
Cited alongside, same era.
ColabFold: making protein folding accessible to all
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger · 2022
Cited alongside, same era.
Categorical SDEs with Simplex Diffusion
Pierre H Richemond, Sander Dieleman, and Arnaud Doucet · 2022
Cited alongside, same era.
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Javier E Santos, Zachary R Fox, Nicholas Lubbers, and Yen Ting Lin · 2023
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Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2023
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Mega-scale experimental analysis of protein folding stability in biology and design
Kotaro Tsuboyama, Justas Dauparas, Jonathan Chen, Elodie Laine, Yasser Mohseni Behbahani, Jonathan J Weinstein, Niall M Mangan, Sergey Ovchinnikov, and Gabriel J Rocklin · 2023
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DiGress: Discrete Denoising diffusion for graph generation
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
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Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, et al · 2023
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De novo design of protein structure and function with RFdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, and Ricky TQ Chen · 2023
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al · 2024
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Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi S. Jaakkola · 2024
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Energy rank alignment: Using preference optimization to search chemical space at scale
Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, and Grant M Rotskoff · 2024
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Protein Discovery with Discrete Walk-Jump Sampling
Nathan C Frey, Daniel Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel, Yan Wu, Stephen Ra, Richard Bonneau, Kyunghyun Cho, et al · 2024
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Discrete flow matching
Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky TQ Chen, Gabriel Synnaeve, Yossi Adi, and Yaron Lipman · 2024
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Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew G Wilson · 2024
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Likelihood-Based Diffusion Language Models
Ishaan Gulrajani and Tatsunori B Hashimoto · 2024
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Generative models for protein structures and sequences
Chloe Hsu, Clara Fannjiang, and Jennifer Listgarten · 2024
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Context-guided diffusion for out-of-distribution molecular and protein design
Leo Klarner, Tim G. J. Rudner, Garrett M. Morris, Charlotte M. Deane, and Yee Whye Teh · 2024
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Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Rohith Krishna, Jue Wang, Woody Ahern, Pascal Sturmfels, Preetham Venkatesh, Indrek Kalvet, Gyu Rie Lee, Felix S Morey-Burrows, Ivan Anishchenko, Ian R Humphreys, et al · 2024
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DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Wei Lu, Jixian Zhang, Weifeng Huang, Ziqiao Zhang, Xiangyu Jia, Zhenyu Wang, Leilei Shi, Chengtao Li, Peter G Wolynes, and Shuangjia Zheng · 2024
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SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
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State-specific protein–ligand complex structure prediction with a multiscale deep generative model
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F Miller III, and Animashree Anandkumar · 2024
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Dirichlet flow matching with applications to dna sequence design
Hannes Stärk, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, and Tommi S. Jaakkola · 2024
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Improving Protein Expression, Stability, and Function with ProteinMPNN
Kiera H Sumida, Reyes Núñez-Franco, Indrek Kalvet, Samuel J Pellock, Basile IM Wicky, Lukas F Milles, Justas Dauparas, Jue Wang, Yakov Kipnis, Noel Jameson, et al · 2024
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Cell-type-directed design of synthetic enhancers
Ibrahim I Taskiran, Katina I Spanier, Hannah Dickmänken, Niklas Kempynck, Alexandra Pančíková, Eren Can Ekşi, Gert Hulselmans, Joy N Ismail, Koen Theunis, Roel Vandepoel, et al · 2024
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Diffusion Language Models Are Versatile Protein Learners
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, and Quanquan Gu · 2024
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Aligning protein generative models with experimental fitness via direct preference optimization
Talal Widatalla, Rafael Rafailov, and Brian Hie · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian Trippe, Christian Naesseth, David Blei, and John P Cunningham · 2024
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