2020

AdaLead: A simple and robust adaptive greedy search algorithm for sequence design

Sinai, Sam, Wang, Richard, Whatley, Alexander et al.

Understand

Efficient design of biological sequences will have a great impact across many industrial and healthcare domains.

However, discovering improved sequences requires solving a difficult optimization problem.
Traditionally, this challenge was approached by biologists through a model-free method known as "directed evolution", the iterative process of random mutation and selection.
As the ability to build models that capture the sequence-to-function map improves, such models can be used as oracles to screen sequences before running experiments.

Built on

No free lunch theorems for optimization
David H Wolpert, William G Macready, et al · 1997
Earlier work this paper cites.
Design by directed evolution
Frances H Arnold · 1998
Earlier work this paper cites.
The effects of steric mutations on the structure of type iii antifreeze protein and its interaction with ice
Carl I DeLuca, Peter L Davies, Qilu Ye, and Zongchao Jia · 1998
Earlier work this paper cites.
The cross-entropy method for combinatorial and continuous optimization
Reuven Rubinstein · 1999
Earlier work this paper cites.
Completely derandomized self-adaptation in evolution strategies
Nikolaus Hansen and Andreas Ostermeier · 2001
Earlier work this paper cites.
Design of a novel globular protein fold with atomic-level accuracy
Brian Kuhlman, Gautam Dantas, Gregory C Ireton, Gabriele Varani, Barry L Stoddard, and David Baker · 2003
Earlier work this paper cites.
Improving catalytic function by ProSAR-driven enzyme evolution
Richard J Fox, S Christopher Davis, Emily C Mundorff, Lisa M Newman, Vesna Gavrilovic, Steven K Ma, Loleta M Chung, Charlene Ching, Sarena Tam, Sheela Muley, et al · 2007
Earlier work this paper cites.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray · 2010
Earlier work this paper cites.
The population genetics of beneficial mutations
H Allen Orr · 2010
Earlier work this paper cites.
ViennaRNA package 2.0
Ronny Lorenz, Stephan H Bernhart, Christian Höner Zu Siederdissen, Hakim Tafer, Christoph Flamm, Peter F Stadler, and Ivo L Hofacker · 2011
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
Original
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Navigating the protein fitness landscape with Gaussian processes
Philip A Romero, Andreas Krause, and Frances H Arnold · 2013
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.
Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.

Similar

Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Cited alongside, same era.
Bayesian optimization for synthetic gene design
Original
Javier Gonzalez, Joseph Longworth, David C James, and Neil D Lawrence · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
On the (un) predictability of a large intragenic fitness landscape
Claudia Bank, Sebastian Matuszewski, Ryan T Hietpas, and Jeffrey D Jensen · 2016
Cited alongside, same era.
Survey of variation in human transcription factors reveals prevalent dna binding changes
Luis A Barrera, Anastasia Vedenko, Jesse V Kurland, Julia M Rogers, Stephen S Gisselbrecht, Elizabeth J Rossin, Jaie Woodard, Luca Mariani, Kian Hong Kock, Sachi Inukai, et al · 2016
Cited alongside, same era.
Wasserstein gan
Original
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Generating and designing DNA with deep generative models
Original
Nathan Killoran, Leo J Lee, Andrew Delong, David Duvenaud, and Brendan J Frey · 2017
Cited alongside, same era.
Deep generative models of genetic variation capture mutation effects
Original
Adam J Riesselman, John B Ingraham, and Debora S Marks · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Original
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Variational auto-encoding of protein sequences
Original
Sam Sinai, Eric Kelsic, George M Church, and Martin A Nowak · 2017
Cited alongside, same era.
Toward machine-guided design of proteins
Surojit Biswas, Gleb Kuznetsov, Pierce J Ogden, Nicholas J Conway, Ryan P Adams, and George M Church · 2018
Cited alongside, same era.
Design by adaptive sampling
Original
David H Brookes and Jennifer Listgarten · 2018
Cited alongside, same era.

Then

Feedback GAN (FBGAN) for DNA: a novel feedback-loop architecture for optimizing protein functions
Original
Anvita Gupta and James Zou · 2018
Later among the works it cites.
Inferring the shape of global epistasis
Jakub Otwinowski, David Martin McCandlish, and Joshua Plotkin · 2018
Later among the works it cites.
Improving online algorithms via ml predictions
Manish Purohit, Zoya Svitkina, and Ravi Kumar · 2018
Later among the works it cites.
Biological sequence design using batched bayesian optimization
David Belanger, Suhani Vora, Zelda Mariet, Ramya Deshpande, David Dohan, Christof Angermueller, Kevin Murphy, Olivier Chapelle, and Lucy Colwell · 2019
Later among the works it cites.
Conditioning by adaptive sampling for robust design
Original
David H Brookes, Hahnbeom Park, and Jennifer Listgarten · 2019
Later among the works it cites.
Implementation matters in deep RL: A case study on PPO and TRPO
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, and Aleksander Madry · 2019
Later among the works it cites.
TF-Agents: A library for reinforcement learning in tensorflow
Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez, Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina, Neal Wu, Efi Kokiopoulou, Luciano Sbaiz, Jamie Smith, Gabor Bartok, Jesse Berent, Chris Harris, Vincent Vanhoucke, and Eugene Brevdo · 2019
Later among the works it cites.
Computational complexity as an ultimate constraint on evolution
Artem Kaznatcheev · 2019
Later among the works it cites.
Information-geometric optimization with natural selection
Jakub Otwinowski and Colin LaMont · 2019
Later among the works it cites.
Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, and Yun S Song · 2019
Later among the works it cites.
Machine-learning-guided directed evolution for protein engineering
Original
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2019
Later among the works it cites.
Model-based reinforcement learning for biological sequence design
Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, and Lucy Colwell · 2020
Closest in time.
Low-n protein engineering with data-efficient deep learning
Surojit Biswas, Grigory Khimulya, Ethan C Alley, Kevin M Esvelt, and George M Church · 2020
Closest in time.
Progen: Language modeling for protein generation
Original
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher · 2020
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…