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Designing protein sequences that fold into a target 3D structure, known as protein inverse folding, is a fundamental challenge in protein engineering.
Inverse protein folding problem: designing polymer sequences
Kaizhi Yue and Ken A Dill · 1992
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Protein design: a hierarchic approach
James W Bryson, Stephen F Betz, Helen S Lu, Daniel J Suich, Hongxing X Zhou, Karyn T O’Neil, and William F DeGrado · 1995
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The empirical case for two systems of reasoning
Steven A Sloman · 1996
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Cath–a hierarchic classification of protein domain structures
Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thornton · 1997
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Who is rational?: Studies of individual differences in reasoning
Keith E Stanovich · 1999
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Representativeness revisited: Attribute substitution in intuitive judgment
Daniel Kahneman, Shane Frederick, et al · 2002
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How root-mean-square distance (rmsd) values depend on the resolution of protein structures that are compared
Oliviero Carugo · 2003
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Tm-align: a protein structure alignment algorithm based on the tm-score
Yang Zhang and Jeffrey Skolnick · 2005
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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Increasing sequence diversity with flexible backbone protein design: the complete redesign of a protein hydrophobic core
Grant S Murphy, Jeffrey L Mills, Michael J Miley, Mischa Machius, Thomas Szyperski, and Brian Kuhlman · 2012
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The coming of age of de novo protein design
Po-Ssu Huang, Scott E Boyken, and David Baker · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al · 2021
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
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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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Alphadesign: A graph protein design method and benchmark on alphafolddb
Zhangyang Gao, Cheng Tan, and Stan Z Li · 2022
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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
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 2022
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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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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Sparks of function by de novo protein design
Alexander E Chu, Tianyu Lu, and Po-Ssu Huang · 2024
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KW-design: Pushing the limit of protein design via knowledge refinement
Zhangyang Gao, Cheng Tan, Xingran Chen, Yijie Zhang, Jun Xia, Siyuan Li, and Stan Z. Li · 2024
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Uniif: Unified molecule inverse folding
Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, and Stan Z Li · 2024
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Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, and Stan Z Li · 2022
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High-resolution de novo structure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al · 2022
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Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, and Stan Z. Li · 2023
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Proteininvbench: Benchmarking protein inverse folding on diverse tasks, models, and metrics
Zhangyang Gao, Cheng Tan, Yijie Zhang, Xingran Chen, Lirong Wu, and Stan Z. Li · 2023
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Protein remote homology detection and structural alignment using deep learning
Tymor Hamamsy, James T Morton, Robert Blackwell, Daniel Berenberg, Nicholas Carriero, Vladimir Gligorijevic, Charlie EM Strauss, Julia Koehler Leman, Kyunghyun Cho, and Richard Bonneau · 2023
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Reasoning with language model is planning with world model
Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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A new age in protein design empowered by deep learning
Hamed Khakzad, Ilia Igashov, Arne Schneuing, Casper Goverde, Michael Bronstein, and Bruno Correia · 2023
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Global-context aware generative protein design
Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, and Stan Z Li · 2023
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Jikun Kang, Xin Zhe Li, Xi Chen, Amirreza Kazemi, and Boxing Chen · 2024
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De novo protein design using geometric vector field networks
Weian Mao, Muzhi Zhu, Zheng Sun, Shuaike Shen, Lin Yuanbo Wu, Hao Chen, and Chunhua Shen · 2024
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Mutual reasoning makes smaller llms stronger problem-solvers
Zhenting Qi, Mingyuan Ma, Jiahang Xu, Li Lyna Zhang, Fan Yang, and Mao Yang · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Q*: Improving multi-step reasoning for llms with deliberative planning, 2024
Chaojie Wang, Yanchen Deng, Zhiyi Lv, Shuicheng Yan, and An Bo · 2024
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An empirical analysis of compute-optimal inference for problem-solving with language models
Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
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Di Zhang, Jiatong Li, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, and Wanli Ouyang · 2024
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Generative ai for controllable protein sequence design: A survey
Yiheng Zhu, Zitai Kong, Jialu Wu, Weize Liu, Yuqiang Han, Mingze Yin, Hongxia Xu, Chang-Yu Hsieh, and Tingjun Hou · 2024
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Simulating 500 million years of evolution with a language model
Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al · 2025
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Fast uncovering of protein sequence diversity from structure
LUCA Silva, Barthelemy Meynard-Piganeau, Carlo Lucibello, Christoph Feinauer, et al · 2025
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