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Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications.
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Pemra Doruker, Ali Rana Atilgan, and Ivet Bahar · 2000
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Gianluca Pollastri, Darisz Przybylski, Burkhard Rost, and Pierre Baldi · 2002
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Porter: a new, accurate server for protein secondary structure prediction
Gianluca Pollastri and Aoife McLysaght · 2005
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Mechanical stretching of proteins—a theoretical survey of the protein data bank
Arata Nakajo, Zacharie Wuillemin, Patrick Metzger, al, Yuxiu Liu, Michael W Murphy, Daniel R Baker, Ana Marija Damjanovi, Burak Koyutürk, Yan-Sheng Li, Joanna I Sułkowska, and Marek Cieplak · 2007
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Hierarchies, multiple energy barriers, and robustness govern the fracture mechanics of α \alpha -helical and β \beta -sheet protein domains
Theodor Ackbarow, Xuefeng Chen, Sinan Keten, and Markus J. Buehler · 2007
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Mateusz Sikora, Joanna I. Sułkowska, and Marek Cieplak · 2009
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Mechanical energy transfer and dissipation in fibrous beta-sheet-rich proteins
Zhiping Xu and Markus J. Buehler · 2010
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Cooperative deformation of hydrogen bonds in beta-strands and beta-sheet nanocrystals
Zhao Qin and Markus J. Buehler · 2010
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Nanomechanics of functional and pathological amyloid materials
Tuomas P.J. Knowles and Markus J. Buehler · 2011
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Prody: Protein dynamics inferred from theory and experiments
Ahmet Bakan, Lidio M. Meireles, and Ivet Bahar · 2011
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Porter, paleale 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility
Claudio Mirabello and Gianluca Pollastri · 2013
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An introduction to convolutional neural networks
Keiron O’Shea and Ryan Nash · 2015
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3dmol.js: molecular visualization with webgl
Nicholas Rego and David Koes · 2015
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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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Attention is all you need
Ashish Vaswani, Google Brain, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Prolango: Protein function prediction using neural machine translation based on a recurrent neural network
Renzhi Cao, Colton Freitas, Leong Chan, Miao Sun, Haiqing Jiang, and Zhangxin Chen · 2017
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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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Deep generative models of genetic variation capture the effects of mutations
Adam J. Riesselman, John B. Ingraham, and Debora S. Marks · 2018
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Prediction of 8-state protein secondary structures by a novel deep learning architecture
Buzhong Zhang, Jinyan Li, and Qiang Lü · 2018
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Generative models for graph-based protein design
John Ingraham, Vikas K Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2019
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Progen: Language modeling for protein generation
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R. Eguchi, Po-Ssu Huang, and Richard Socher · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam Mccandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Yu-Chuan Hsu, Chi-Hua Yu, and Markus J Buehler · 2020
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Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
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Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling
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Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou · 2022
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End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks
Eric L. Buehler and Markus J. Buehler · 2022
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Wei Lu, Zhenze Yang, and Markus J. Buehler · 2022
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Zhenze Yang and Markus J. Buehler · 2022
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Chi Hua Yu and Markus J. Buehler · 2020
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Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray · 2020
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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De novo protein design by deep network hallucination
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Program synthesis with large language models
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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, Basile I.M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 2023
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Melm, a generative pretrained language modeling framework that solves forward and inverse mechanics problems
Markus J. Buehler · 2023
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Fine-tuning large neural language models for biomedical natural language processing highlights d systematic exploration of fine-tuning stability in biomedical nlp d domain-specific vocabulary and pretraining facilitate robust models for fine-tuning d pubmedbert-large and pubmedelectra models advance state-of-the-art in biomedical nlp in brief fine-tuning large neural language models for biomedical natural language processing
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Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
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Materials informatics tools in the context of bio-inspired material mechanics
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Generative modeling, design, and analysis of spider silk protein sequences for enhanced mechanical properties
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Building cooperative embodied agents modularly with large language models
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Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins
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