Fetching the paper…
Reading the bibliography…
Inverse folding models play an important role in structure-based design by predicting amino acid sequences that fold into desired reference structures.
The protein folding problem
Ken A. Dill, S. Banu Ozkan, M. Scott Shell, and Thomas R. Weikl · 1936
Earlier work this paper cites.
On information and sufficiency
Kullback, S. and Leibler, R. A · 1951
Earlier work this paper cites.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
Earlier work this paper cites.
The building of protein structures from alpha-carbon coordinates
Paul E. Correa · 1990
Earlier work this paper cites.
Inverse protein folding problem: designing polymer sequences
Kaizhi Yue and Ken A Dill · 1992
Earlier work this paper cites.
De novo and inverse folding predictions of protein structure and dynamics
A Godzik, A Kolinski, and J Skolnick · 1993
Earlier work this paper cites.
De novo protein design. i. in search of stability and specificity11edited by f. e. cohen
Patrice Koehl and Michael Levitt · 1999
Earlier work this paper cites.
Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2004
Earlier work this paper cites.
Design and application of stimulus-responsive peptide systems
Karuppiah Chockalingam, Mark Blenner, and Scott Banta · 2007
Earlier work this paper cites.
Predicting the conformations of peptides and proteins in early evolution. a review article submitted to biology direct
E James Milner-White and Michael J Russell · 2008
Earlier work this paper cites.
Designing peptide based nanomaterials
Rein V Ulijn and Andrew M Smith · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey · 2008
Earlier work this paper cites.
Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes
Axel Tiessen, Paulino Pérez-Rodríguez, and Luis José Delaye-Arredondo · 2012
Earlier work this paper cites.
Cell-penetrating peptides: design, synthesis, and applications
Dana Maria Copolovici, Kent Langel, Elo Eriste, and Ulo Langel · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Soeding · 2017
Cited alongside, same era.
Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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
Later among the works it cites.
Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
Later among the works it cites.
Preference optimization for molecular language models, 2023
Ryan Park, Ryan Theisen, Navriti Sahni, Marcel Patek, Anna Cichońska, and Rayees Rahman · 2023
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marcelo DT Torres, Shanmugapriya Sothiselvam, Timothy K Lu, and Cesar de la Fuente-Nunez · 2019
Cited alongside, same era.
Macromolecular modeling and design in rosetta: recent methods and frameworks
Julia Koehler Leman, Brian D Weitzner, Steven M Lewis, Jared Adolf-Bryfogle, Nawsad Alam, Rebecca F Alford, Melanie Aprahamian, David Baker, Kyle A Barlow, Patrick Barth, Benjamin Basanta, Brian J Bender, Kristin Blacklock, Jaume Bonet, Scott E Boyken, Phil Bradley, Chris Bystroff, Patrick Conway, Seth Cooper, Bruno E Correia, Brian Coventry, Rhiju Das, René M De Jong, Frank DiMaio, Lorna Dsilva, Roland Dunbrack, Alexander S Ford, Brandon Frenz, Darwin Y Fu, Caleb Geniesse, Lukasz Goldschmidt, Ragul Gowthaman, Jeffrey J Gray, Dominik Gront, Sharon Guffy, Scott Horowitz, Po-Ssu Huang, Thomas Huber, Tim M Jacobs, Jeliazko R Jeliazkov, David K Johnson, Kalli Kappel, John Karanicolas, Hamed Khakzad, Karen R Khar, Sagar D Khare, Firas Khatib, Alisa Khramushin, Indigo C King, Robert Kleffner, Brian Koepnick, Tanja Kortemme, Georg Kuenze, Brian Kuhlman, Daisuke Kuroda, Jason W Labonte, Jason K Lai, Gideon Lapidoth, Andrew Leaver-Fay, Steffen Lindert, Thomas Linsky, Nir London, Joseph H Lubin, Sergey Lyskov, Jack Maguire, Lars Malmström, Enrique Marcos, Orly Marcu, Nicholas A Marze, Jens Meiler, Rocco Moretti, Vikram Khipple Mulligan, Santrupti Nerli, Christoffer Norn, Shane Ó’Conchúir, Noah Ollikainen, Sergey Ovchinnikov, Michael S Pacella, Xingjie Pan, Hahnbeom Park, Ryan E Pavlovicz, Manasi Pethe, Brian G Pierce, Kala Bharath Pilla, Barak Raveh, P Douglas Renfrew, Shourya S Roy Burman, Aliza Rubenstein, Marion F Sauer, Andreas Scheck, William Schief, Ora Schueler-Furman, Yuval Sedan, Alexander M Sevy, Nikolaos G Sgourakis, Lei Shi, Justin B Siegel, Daniel-Adriano Silva, Shannon Smith, Yifan Song, Amelie Stein, Maria Szegedy, Frank D Teets, Summer B Thyme, Ray Yu-Ruei Wang, Andrew Watkins, Lior Zimmerman, and Richard Bonneau · 2020
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, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Cited alongside, same era.
OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-En Andrew Ban, Peter K Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, and Mohammed AlQuraishi · 2022
Cited alongside, same era.
Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Pablo Chacón, and Stan Z Li · 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.
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.
Inverse folding of protein complexes with a structure-informed language model enables unsupervised antibody evolution
Varun R. Shanker, Theodora U.J. Bruun, Brian L. Hie, and Peter S. Kim · 2023
Later among the works it cites.
Chaoqi Wang, Yibo Jiang, Chenghao Yang, Han Liu, and Yuxin Chen · 2023
Later among the works it cites.
Graph denoising diffusion for inverse protein folding
Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Liò, and Yu Guang Wang · 2023
Later among the works it cites.
Preference optimization of protein language models as a multi-objective binder design paradigm, 2024
Pouria Mistani and Venkatesh Mysore · 2024
Closest in time.
Disentangling length from quality in direct preference optimization, 2024
Ryan Park, Rafael Rafailov, Stefano Ermon, and Chelsea Finn · 2024
Closest in time.
Aligning protein generative models with experimental fitness via direct preference optimization
Talal Widatalla, Rafael Rafailov, and Brian Hie · 2024
Closest in time.
Beyond one-preference-fits-all alignment: Multi-objective direct preference optimization, 2024
Zhanhui Zhou, Jie Liu, Jing Shao, Xiangyu Yue, Chao Yang, Wanli Ouyang, and Yu Qiao · 2024
Closest in time.
The past, present and future of protein-based materials
Nadia C. Abascal and Lynne Regan · 2046
Closest in time.
Alphafold2 and its applications in the fields of biology and medicine
Zhenyu Yang, Xiaoxi Zeng, Yi Zhao, and Runsheng Chen · 2059
Closest in time.