Fetching the paper…
Reading the bibliography…
Designing protein sequences with desired biological function is crucial in biology and chemistry.
Theoretical studies of protein folding
Go, N · 1983
Earlier work this paper cites.
Principles that determine the structure of proteins
Chothia, C · 1984
Earlier work this paper cites.
The nk model of rugged fitness landscapes and its application to maturation of the immune response
Kauffman, S. A. and Weinberger, E. D · 1989
Earlier work this paper cites.
A monte carlo implementation of the em algorithm and the poor man’s data augmentation algorithms
Wei, G. C. and Tanner, M. A · 1990
Earlier work this paper cites.
Design by directed evolution
Arnold, F. H · 1998
Earlier work this paper cites.
Completely derandomized self-adaptation in evolution strategies
Hansen, N. and Ostermeier, A · 2001
Earlier work this paper cites.
The production of recombinant pharmaceutical proteins in plants
Ma, J. K., Drake, P. M., and Christou, P · 2003
Earlier work this paper cites.
Monte Carlo statistical methods
Robert, C. and Casella, G · 2004
Earlier work this paper cites.
The importance of sequence diversity in the aggregation and evolution of proteins
Wright, C. F., Teichmann, S. A., Clarke, J., and Dobson, C. M · 2005
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, C. M · 2006
Earlier work this paper cites.
The cma evolution strategy: a comparing review
Hansen, N · 2006
Earlier work this paper cites.
Improving catalytic function by prosar-driven enzyme evolution
Fox, R. J., Davis, S. C., Mundorff, E. C., Newman, L. M., Gavrilovic, V., Ma, S. K., Chung, L. M., Ching, C., Tam, S., Muley, S., et al · 2007
Earlier work this paper cites.
Linking the functions of unrelated proteins using a novel directed evolution domain insertion method
Edwards, W. R., Busse, K., Allemann, R. K., and Jones, D. D · 2008
Earlier work this paper cites.
In the light of directed evolution: pathways of adaptive protein evolution
Bloom, J. D. and Arnold, F. H · 2009
Earlier work this paper cites.
Exploring protein fitness landscapes by directed evolution
Romero, P. A. and Arnold, F. H · 2009
Earlier work this paper cites.
Random mutagenesis methods for in vitro directed enzyme evolution
Labrou, N. E · 2010
Earlier work this paper cites.
Information theory: coding theorems for discrete memoryless systems
Csiszár, I. and Körner, J · 2011
Earlier work this paper cites.
Strategy and success for the directed evolution of enzymes
Dalby, P. A · 2011
Earlier work this paper cites.
Assessing the utility of coevolution-based residue–residue contact predictions in a sequence-and structure-rich era
Kamisetty, H., Ovchinnikov, S., and Baker, D · 2013
Earlier work this paper cites.
Deep mutational scanning of an rrm domain of the saccharomyces cerevisiae poly (a)-binding protein
Melamed, D., Young, D. L., Gamble, C. E., Miller, C. R., and Fields, S · 2013
Earlier work this paper cites.
Activity-enhancing mutations in an e3 ubiquitin ligase identified by high-throughput mutagenesis
Starita, L. M., Pruneda, J. N., Lo, R. S., Fowler, D. M., Kim, H. J., Hiatt, J. B., Shendure, J., Brzovic, P. S., Fields, S., and Klevit, R. E · 2013
Earlier work this paper cites.
A comprehensive, high-resolution map of a gene’s fitness landscape
Firnberg, E., Labonte, J. W., Gray, J. J., and Ostermeier, M · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Ccmpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
Seemayer, S., Gruber, M., and Söding, J · 2014
Cited alongside, same era.
The phyre2 web portal for protein modeling, prediction and analysis
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., and Sternberg, M. J · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Comprehensive sequence-flux mapping of a levoglucosan utilization pathway in e. coli
Klesmith, J. R., Bacik, J.-P., Michalczyk, R., and Whitehead, T. A · 2015
Cited alongside, same era.
Methods for the directed evolution of proteins
Packer, M. S. and Liu, D. R · 2015
Cited alongside, same era.
Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., et al · 2016
Evaluating protein transfer learning with TAPE
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, P., Canny, J. F., Abbeel, P., and Song, Y. S · 2019
Later among the works it cites.
Model-based reinforcement learning for biological sequence design
Angermüller, C., Dohan, D., Belanger, D., Deshpande, R., Murphy, K., and Colwell, L · 2020
Later among the works it cites.
Model inversion networks for model-based optimization
Kumar, A. and Levine, S · 2020
Later among the works it cites.
Optimus: Organizing sentences via pre-trained modeling of a latent space
Li, C., Gao, X., Li, Y., Peng, B., Li, X., Zhang, Y., and Gao, J · 2020
Later among the works it cites.
Progen: Language modeling for protein generation
Madani, A., McCann, B., Naik, N., Keskar, N. S., Anand, N., Eguchi, R. R., Huang, P.-S., and Socher, R · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Plant protein phosphatases 2c: from genomic diversity to functional multiplicity and importance in stress management
Singh, A., Pandey, A., Srivastava, A. K., Tran, L.-S. P., and Pandey, G. K · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Cited alongside, same era.
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P., Springer, M., Sander, C., and Marks, D. S · 2017
Cited alongside, same era.
Recent advances in (therapeutic protein) drug development
Lagassé, H. D., Alexaki, A., Simhadri, V. L., Katagiri, N. H., Jankowski, W., Sauna, Z. E., and Kimchi-Sarfaty, C · 2017
Cited alongside, same era.
Exploring protein sequence–function landscapes
Starr, T. N. and Thornton, J. W · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Moss, H. B., Leslie, D. S., Beck, D., Gonzalez, J., and Rayson, P · 2020
Later among the works it cites.
Amortized bayesian optimization over discrete spaces
Swersky, K., Rubanova, Y., Dohan, D., and Murphy, K · 2020
Later among the works it cites.
Low-n protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., and Church, G. M · 2021
Later among the works it cites.
Deep diversification of an aav capsid protein by machine learning
Bryant, D. H., Bashir, A., Sinai, S., Jain, N. K., Ogden, P. J., Riley, P. F., Church, G. M., Colwell, L. J., and Kelsic, E. D · 2021
Later among the works it cites.
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations
Das, P., Sercu, T., Wadhawan, K., Padhi, I., Gehrmann, S., Cipcigan, F., Chenthamarakshan, V., Strobelt, H., Dos Santos, C., Chen, P.-Y., et al · 2021
Later among the works it cites.
Ecnet is an evolutionary context-integrated deep learning framework for protein engineering
Luo, Y., Jiang, G., Yu, T., Liu, Y., Vo, L., Ding, H., Su, Y., Qian, W. W., Zhao, H., and Peng, J · 2021
Later among the works it cites.
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., and Rives, A · 2021
Later among the works it cites.
Benchmarking deep generative models for diverse antibody sequence design
Melnyk, I., Das, P., Vijil, V., and Lozano, A · 2021
Later among the works it cites.
Glancing transformer for non-autoregressive neural machine translation
Qian, L., Zhou, H., Bao, Y., Wang, M., Qiu, L., Zhang, W., Yu, Y., and Li, L · 2021
Later among the works it cites.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al · 2021
Later among the works it cites.
Black-box optimization for automated discovery
Terayama, K., Sumita, M., Tamura, R., and Tsuda, K · 2021
Later among the works it cites.
Optimizing molecules using efficient queries from property evaluations
Hoffman, S. C., Chenthamarakshan, V., Wadhawan, K., Chen, P.-Y., and Das, P · 2022
Later among the works it cites.
Biological sequence design with gflownets
Jain, M., Bengio, E., Hernández-García, A., Rector-Brooks, J., Dossou, B. F. P., Ekbote, C. A., Fu, J., Zhang, T., Kilgour, M., Zhang, D., Simine, L., Das, P., and Bengio, Y · 2022
Later among the works it cites.
Proximal exploration for model-guided protein sequence design
Ren, Z., Li, J., Ding, F., Zhou, Y., Ma, J., and Peng, J · 2022
Later among the works it cites.
Design-bench: Benchmarks for data-driven offline model-based optimization
Trabucco, B., Geng, X., Kumar, A., and Levine, S · 2022
Later among the works it cites.
Unifying likelihood-free inference with black-box optimization and beyond
Zhang, D., Fu, J., Bengio, Y., and Courville, A. C · 2022
Later among the works it cites.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
Closest in time.