Fetching the paper…
Reading the bibliography…
Here we present a novel approach to protein design and phenotypic inference using a generative model for protein sequences.
Functional proteins from a random-sequence library
Anthony D Keefe and Jack W Szostak · 2001
Earlier work this paper cites.
Computational design of an enzyme catalyst for a stereoselective bimolecular diels-alder reaction
Justin B Siegel, Alexandre Zanghellini, Helena M Lovick, Gert Kiss, Abigail R Lambert, Jennifer L St Clair, Jasmine L Gallaher, Donald Hilvert, Michael H Gelb, Barry L Stoddard, Kendall N Houk, Forrest E Michael, and David Baker · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Engineered thermostable fungal cellulases exhibit efficient synergistic cellulose hydrolysis at elevated temperatures
Devin L Trudeau, Toni M Lee, and Frances H Arnold · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Computational protein design enables a novel one-carbon assimilation pathway
Justin B Siegel, Amanda Lee Smith, Sean Poust, Adam J Wargacki, Arren Bar-Even, Catherine Louw, Betty W Shen, Christopher B Eiben, Huu M Tran, Elad Noor, Jasmine L Gallaher, Jacob Bale, Yasuo Yoshikuni, Michael H Gelb, Jay D Keasling, Barry L Stoddard, Mary E Lidstrom, and David Baker · 2015
Earlier work this paper cites.
Expanding the enzyme universe: accessing non-natural reactions by mechanism-guided directed evolution
Hans Renata, Z Jane Wang, and Frances H Arnold · 2015
Earlier work this paper cites.
Synthetic protein switches: design principles and applications
Viktor Stein and Kirill Alexandrov · 2015
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
Earlier work this paper cites.
The coming of age of de novo protein design
Po-Ssu Huang, Scott E Boyken, and David Baker · 2016
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
PixelVAE: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Conditional image generation with PixelCNN decoders
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
WaveNet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
Deepre: sequence-based enzyme ec number prediction by deep learning
Yu Li, Sheng Wang, Ramzan Umarov, Bingqing Xie, Ming Fan, Lihua Li, and Xin Gao · 2017
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
Neural autoregressive flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
Later among the works it cites.
Deep generative modeling for single-cell transcriptomics
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, and Nir Yosef · 2018
Later among the works it cites.
Directed evolution: bringing new chemistry to life
Frances H Arnold · 2018
Later among the works it cites.
Directed evolution mimics allosteric activation by stepwise tuning of the conformational ensemble
Andrew R Buller, Paul van Roye, Jackson K B Cahn, Remkes A Scheele, Michael Herger, and Frances H Arnold · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aäron van den Oord, Alex Graves, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
How many protein sequences fold to a given structure? a coevolutionary analysis
Pengfei Tian and Robert B Best · 2017
Cited alongside, same era.
Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Computational protein design: a review
Ivan Coluzza · 2017
Cited alongside, same era.
Dilated residual networks
Fisher Yu, Vladlen Koltun, and Thomas Funkhouser · 2017
Cited alongside, same era.
InfoVAE: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
Cited alongside, same era.
Later among the works it cites.
Deep generative models of genetic variation capture the effects of mutations
Adam J Riesselman, John B Ingraham, and Debora S Marks · 2018
Later among the works it cites.
Binning microbial genomes using deep learning
Jakob Nybo Nissen, Casper Kaae Sonderby, Jose Juan Almagro Armenteros, Christopher Heje Groenbech, Henrik Bjorn Nielsen, Thomas Nordahl Petersen, Ole Winther, and Simon Rasmussen · 2018
Later among the works it cites.
Machine learning in protein engineering
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2018
Later among the works it cites.
UniProt: the universal protein knowledgebase
The UniProt Consortium · 2018
Later among the works it cites.
Clustering huge protein sequence sets in linear time
Martin Steinegger and Johannes Söding · 2018
Later among the works it cites.
The information autoencoding family: A lagrangian perspective on latent variable generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2018
Later among the works it cites.
De novo design of potent and selective mimics of IL-2 and IL-15
Daniel-Adriano Silva, Shawn Yu, Umut Y. Ulge, Jamie B. Spangler, Kevin M. Jude, Carlos Labão-Almeida, Lestat R. Ali, Alfredo Quijano-Rubio, Mikel Ruterbusch, Isabel Leung, Tamara Biary, Stephanie J. Crowley, Enrique Marcos, Carl D. Walkey, Brian D. Weitzner, Fátima Pardo-Avila, Javier Castellanos, Lauren Carter, Lance Stewart, Stanley R. Riddell, Marion Pepper, Gonçalo J. L. Bernardes, Michael Dougan, K. Christopher Garcia, and David Baker · 2019
Closest in time.
Feedback gan for dna optimizes protein functions
Anvita Gupta and James Zou · 2019
Closest in time.