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This work presents RNAdiffusion, a latent diffusion model for generating and optimizing discrete RNA sequences of variable lengths.
Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein et al · 1966
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Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Time reversal of diffusions
Ulrich G Haussmann and Etienne Pardoux · 1986
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Ab initio protein structure prediction of casp iii targets using rosetta
Kim T Simons, Rich Bonneau, Ingo Ruczinski, and David Baker · 1999
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Rna: versatility in form and function
Mark G Caprara and Timothy W Nilsen · 2000
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Rna secondary structure: physical and computational aspects
Paul G Higgs · 2000
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Correlations between mrna expression levels and gc contents of coding and untranslated regions of genes in rodents
Özlen Konu and Ming D Li · 2002
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling
Nicholas T Ingolia, Sina Ghaemmaghami, John RS Newman, and Jonathan S Weissman · 2009
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Rna worlds: from life’s origins to diversity in gene regulation
John F Atkins, Raymond F Gesteland, and Thomas Cech · 2011
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Viennarna package 2.0
Ronny Lorenz, Stephan H Bernhart, Christian Höner zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F Stadler, and Ivo L Hofacker · 2011
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Before it gets started: regulating translation at the 5’ utr
Patricia R Araujo, Kihoon Yoon, Daijin Ko, Andrew D Smith, Mei Qiao, Uthra Suresh, Suzanne C Burns, Luiz OF Penalva, et al · 2012
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Cd-hit: accelerated for clustering the next-generation sequencing data
Limin Fu, Beifang Niu, Zhengwei Zhu, Sitao Wu, and Weizhong Li · 2012
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On the normalization of the minimum free energy of rnas by sequence length
Edoardo Trotta · 2014
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A two-phase binning algorithm using l-mer frequency on groups of non-overlapping reads
Le Van Vinh, Tran Van Lang, Le Thanh Binh, and Tran Van Hoai · 2015
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Rna structure: advances and assessment of 3d structure prediction
Zhichao Miao and Eric Westhof · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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Design of metalloproteins and novel protein folds using variational autoencoders
Joe G Greener, Lewis Moffat, and David T Jones · 2018
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Semi-amortized variational autoencoders
Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, and Alexander Rush · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Fully differentiable full-atom protein backbone generation
Namrata Anand, Raphael Eguchi, and Po-Ssu Huang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Lagging inference networks and posterior collapse in variational autoencoders
Junxian He, Daniel Spokoyny, Graham Neubig, and Taylor Berg-Kirkpatrick · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Human 5’ utr design and variant effect prediction from a massively parallel translation assay
Paul J Sample, Ban Wang, David W Reid, Vlad Presnyak, Iain J McFadyen, David R Morris, and Georg Seelig · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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De novo protein design for novel folds using guided conditional wasserstein generative adversarial networks
Mostafa Karimi, Shaowen Zhu, Yue Cao, and Yang Shen · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Optimus: Organizing sentences via pre-trained modeling of a latent space
Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, and Jianfeng Gao · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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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 · 2020
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Nucleic acids research , 49(D1):D212–D220, 2021
Rnacentral 2021: secondary structure integration, improved sequence search and new member databases · 2021
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De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J Pellock, Tamuka M Chidyausiku, Theresa A Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K Bera, et al · 2021
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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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High-throughput 5’ utr engineering for enhanced protein production in non-viral gene therapies
Jicong Cao, Eva Maria Novoa, Zhizhuo Zhang, William CW Chen, Dianbo Liu, Gigi CG Choi, Alan SL Wong, Claudia Wehrspaun, Manolis Kellis, and Timothy K Lu · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Autoregressive diffusion models
Emiel Hoogeboom, Alexey A Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans · 2021
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Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome
Yanrong Ji, Zhihan Zhou, Han Liu, and Ramana V Davuluri · 2021
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Beyond in-place corruption: Insertion and deletion in denoising probabilistic models
Daniel D Johnson, Jacob Austin, Rianne van den Berg, and Daniel Tarlow · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Predicting mean ribosome load for 5’utr of any length using deep learning
Alexander Karollus, Žiga Avsec, and Julien Gagneur · 2021
Cited alongside, same era.
Deep generative models create new and diverse protein structures
Zeming Lin, Tom Sercu, Yann LeCun, and Alexander Rives · 2021
Cited alongside, same era.
Versatile diffusion: Text, images and variations all in one diffusion model
Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, and Humphrey Shi · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B. Tenenbaum, Tommi S. Jaakkola, and Pulkit Agrawal · 2023
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Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, and Kevin K Yang · 2023
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Finetuning pretrained transformers into variational autoencoders
Seongmin Park and Jihwa Lee · 2021
Cited alongside, same era.
Msa transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
Step-unrolled denoising autoencoders for text generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, and Aaron van den Oord · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
Cited alongside, same era.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 2022
Cited alongside, same era.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Dirichlet diffusion score model for biological sequence generation
Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, and Jian Zhou · 2023
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Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Utrgan: Learning to generate 5’utr sequences for optimized translation efficiency and gene expression
Sina Barazandeh, Furkan Ozden, Ahmet Hincer, Urartu Ozgur Safak Seker, and A Ercument Cicek · 2023
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Dna language models are powerful predictors of genome-wide variant effects
Gonzalo Benegas, Sanjit Singh Batra, and Yun S Song · 2023
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Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
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Minshuo Chen, Kaixuan Huang, Tuo Zhao, and Mengdi Wang · 2023
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Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2023
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Protein design with guided discrete diffusion
Nate Gruver, Samuel Stanton, Nathan C Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew Gordon Wilson · 2023
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Ssd-2: Scaling and inference-time fusion of diffusion language models
Xiaochuang Han, Sachin Kumar, Yulia Tsvetkov, and Marjan Ghazvininejad · 2023
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Illuminating protein space with a programmable generative model
John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent Frappier, Dana M Lord, Christopher Ng-Thow-Hing, Erik R Van Vlack, et al · 2023
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Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
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Adaptdiffuser: Diffusion models as adaptive self-evolving planners
Zhixuan Liang, Yao Mu, Mingyu Ding, Fei Ni, Masayoshi Tomizuka, and Ping Luo · 2023
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Yeqing Lin and Mohammed AlQuraishi · 2023
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Text generation with diffusion language models: A pre-training approach with continuous paragraph denoise
Zhenghao Lin, Yeyun Gong, Yelong Shen, Tong Wu, Zhihao Fan, Chen Lin, Nan Duan, and Weizhu Chen · 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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Progen2: exploring the boundaries of protein language models
Erik Nijkamp, Jeffrey A Ruffolo, Eli N Weinstein, Nikhil Naik, and Ali Madani · 2023
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Diffusion models are minimax optimal distribution estimators
Kazusato Oko, Shunta Akiyama, and Taiji Suzuki · 2023
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Rnagen: A generative adversarial network-based model to generate synthetic rna sequences to target proteins
Furkan Ozden, Sina Barazandeh, Dogus Akboga, Urartu Ozgur Safak Seker, and A Ercument Cicek · 2023
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Prot-vae: Protein transformer variational autoencoder for functional protein design
Emre Sevgen, Joshua Moller, Adrian Lange, John Parker, Sean Quigley, Jeff Mayer, Poonam Srivastava, Sitaram Gayatri, David Hosfield, Maria Korshunova, Micha Livne, Michelle Gill, Rama Ranganathan, Anthony B. Costa, and Andrew L. Ferguson · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Dior-cvae: Diffusion priors in variational dialog generation
Tianyu Yang, Thy Thy Tran, and Iryna Gurevych · 2023
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Diffusion language models can perform many tasks with scaling and instruction-finetuning
Jiasheng Ye, Zaixiang Zheng, Yu Bao, Lihua Qian, and Quanquan Gu · 2023
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Se (3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
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Planner: Generating diversified paragraph via latent language diffusion model
Yizhe Zhang, Jiatao Gu, Zhuofeng Wu, Shuangfei Zhai, Josh Susskind, and Navdeep Jaitly · 2023
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Translation rate prediction and regulatory motif discovery with multi-task learning
Weizhong Zheng, John HC Fong, Yuk Kei Wan, Athena HY Chu, Yuanhua Huang, Alan SL Wong, and Joshua WK Ho · 2023
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A 5’ utr language model for decoding untranslated regions of mrna and function predictions
Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, and Mengdi Wang · 2024
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Dna-diffusion: Leveraging generative models for controlling chromatin accessibility and gene expression via synthetic regulatory elements
Lucas Ferreira DaSilva, Simon Senan, Zain Munir Patel, Aniketh Janardhan Reddy, Sameer Gabbita, Zach Nussbaum, Cesar Miguel Valdez Cordova, Aaron Wenteler, Noah Weber, Tin M Tunjic, et al · 2024
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Likelihood-based diffusion language models
Ishaan Gulrajani and Tatsunori B Hashimoto · 2024
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Discdiff: Latent diffusion model for dna sequence generation
Zehui Li, Yuhao Ni, William AV Beardall, Guoxuan Xia, Akashaditya Das, Guy-Bart Stan, and Yiren Zhao · 2024
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Roformer: Enhanced transformer with rotary position embedding
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Protein structure generation via folding diffusion
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
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Large-scale reinforcement learning for diffusion models
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