Fetching the paper…
Reading the bibliography…
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling.
Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels
Michael Emmerich · 2005
Earlier work this paper cites.
Uniref: comprehensive and non-redundant uniprot reference clusters
Baris E Suzek, Hongzhan Huang, Peter McGarvey, Raja Mazumder, and Cathy H Wu · 2007
Earlier work this paper cites.
Biopython: freely available python tools for computational molecular biology and bioinformatics
Peter JA Cock, Tiago Antao, Jeffrey T Chang, Brad A Chapman, Cymon J Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, et al · 2009
Earlier work this paper cites.
Exploring protein fitness landscapes by directed evolution
Philip A Romero and Frances H Arnold · 2009
Earlier work this paper cites.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Hypervolume-based expected improvement: Monotonicity properties and exact computation
Michael TM Emmerich, André H Deutz, and Jan Willem Klinkenberg · 2011
Earlier work this paper cites.
Her2: biology, detection, and clinical implications
Carolina Gutierrez and Rachel Schiff · 2011
Earlier work this paper cites.
Introduction to protein structure
Carl Ivar Branden and John Tooze · 2012
Earlier work this paper cites.
Construction of a rationally designed antibody platform for sequencing-assisted selection
H Benjamin Larman, George Jing Xu, Natalya N Pavlova, and Stephen J Elledge · 2012
Earlier work this paper cites.
The application of next generation sequencing to the understanding of antibody repertoires
Pascale Mathonet and Christopher G Ullman · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
A survey on direct search methods for blackbox optimization and their applications
Charles Audet · 2014
Earlier work this paper cites.
Sabdab: the structural antibody database
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M Deane · 2014
Earlier work this paper cites.
Anarci: antigen receptor numbering and receptor classification
James Dunbar and Charlotte M Deane · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
Earlier work this paper cites.
Protein data bank (pdb): the single global macromolecular structure archive
Stephen K Burley, Helen M Berman, Gerard J Kleywegt, John L Markley, Haruki Nakamura, and Sameer Velankar · 2017
Earlier work this paper cites.
Plug & play generative networks: Conditional iterative generation of images in latent space
Anh Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
Earlier work this paper cites.
The reparameterization trick for acquisition functions
James T Wilson, Riccardo Moriconi, Frank Hutter, and Marc Peter Deisenroth · 2017
Earlier work this paper cites.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Earlier work this paper cites.
Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2018
Earlier work this paper cites.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
Earlier work this paper cites.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
Earlier work this paper cites.
Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2019
Earlier work this paper cites.
Theory of evolutionary computation: Recent developments in discrete optimization
Benjamin Doerr and Frank Neumann · 2019
Earlier work this paper cites.
Mask-predict: Parallel decoding of conditional masked language models
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer · 2019
Earlier work this paper cites.
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Samuel Daulton, Maximilian Balandat, and Eytan Bakshy · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Adalead: A simple and robust adaptive greedy search algorithm for sequence design
Sam Sinai, Richard Wang, Alexander Whatley, Stewart Slocum, Elina Locane, and Eric D Kelsic · 2020
Cited alongside, same era.
Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Later among the works it cites.
A penultimate classification of canonical antibody cdr conformations
Simon Kelow, Bulat Faezov, Qifang Xu, Mitchell I Parker, Jared Adolf-Bryfogle, and Roland L Dunbrack Jr · 2022
Later among the works it cites.
Proteinsgm: Score-based generative modeling for de novo protein design
Jin Sub Lee and Philip M Kim · 2022
Later among the works it cites.
Diffusion-lm improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B Hashimoto · 2022
Later among the works it cites.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jasmijn Bastings, Sebastian Ebert, Polina Zablotskaia, Anders Sandholm, and Katja Filippova · 2021
Cited alongside, same era.
Generalization in nli: Ways (not) to go beyond simple heuristics, 2021
Prajjwal Bhargava, Aleksandr Drozd, and Anna Rogers · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Function-guided protein design by deep manifold sampling
Vladimir Gligorijevic, Daniel Berenberg, Stephen Ra, Andrew Watkins, Simon Kelow, Kyunghyun Cho, and Richard Bonneau · 2021
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
Machine learning for perturbational single-cell omics
Yuge Ji, Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis · 2021
Cited alongside, same era.
Benchmarking deep generative models for diverse antibody sequence design
Igor Melnyk, Payel Das, Vijil Chenthamarakshan, and Aurelie Lozano · 2021
Cited alongside, same era.
Antigen-specific antibody design and optimization with diffusion-based generative models
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
Later among the works it cites.
Observed antibody space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences
Tobias H Olsen, Fergus Boyles, and Charlotte M Deane · 2022
Later among the works it cites.
Diffuser: Discrete diffusion via edit-based reconstruction
Machel Reid, Vincent J Hellendoorn, and Graham Neubig · 2022
Later among the works it cites.
Proximal exploration for model-guided protein sequence design
Zhizhou Ren, Jiahan Li, Fan Ding, Yuan Zhou, Jianzhu Ma, and Jian Peng · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Jeffrey A Ruffolo and Jeffrey J Gray · 2022
Later among the works it cites.
Efficient training of low-curvature neural networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, and François Fleuret · 2022
Later among the works it cites.
Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson · 2022
Later among the works it cites.
Self-conditioned embedding diffusion for text generation
Robin Strudel, Corentin Tallec, Florent Altché, Yilun Du, Yaroslav Ganin, Arthur Mensch, Will Grathwohl, Nikolay Savinov, Sander Dieleman, Laurent Sifre, et al · 2022
Later among the works it cites.
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2022
Later among the works it cites.
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, Basile IM Wicky, Lukas F Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives · 2022
Later among the works it cites.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
Later among the works it cites.
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
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, et al · 2022
Later among the works it cites.
Benchmarking interpretability tools for deep neural networks
Stephen Casper, Yuxiao Li, Jiawei Li, Tong Bu, Kevin Zhang, and Dylan Hadfield-Menell · 2023
Closest in time.
Muse: Text-to-image generation via masked generative transformers
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al · 2023
Closest in time.
Plug & play directed evolution of proteins with gradient-based discrete mcmc
Patrick Emami, Aidan Perreault, Jeffrey Law, David Biagioni, and Peter St John · 2023
Closest in time.
Learning protein family manifolds with smoothed energy-based models
Nathan C. Frey, Dan Berenberg, Joseph Kleinhenz, Isidro Hotzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Stephen Ra, Richard Bonneau, Kyunghyun Cho, Andreas Loukas, Vladimir Gligorijevic, and Saeed Saremi · 2023
Closest in time.
Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De · 2023
Closest in time.
Extrapolative controlled sequence generation via iterative refinement
Vishakh Padmakumar, Richard Yuanzhe Pang, He He, and Ankur P Parikh · 2023
Closest in time.
Hyena hierarchy: Towards larger convolutional language models
Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, and Christopher Ré · 2023
Closest in time.
Unlocking de novo antibody design with generative artificial intelligence
Amir Shanehsazzadeh, Sharrol Bachas, Matt McPartlon, George Kasun, John M Sutton, Andrea K Steiger, Richard Shuai, Christa Kohnert, Goran Rakocevic, Jahir M Gutierrez, et al · 2023
Closest in time.
Raghav Singhal, Mark Goldstein, and Rajesh Ranganath · 2023
Closest in time.