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Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science.
A general method applicable to the search for similarities in the amino acid sequence of two proteins
Needleman, S. B. and Wunsch, C. D · 1970
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Amino acid substitution matrices from protein blocks
Henikoff, S. and Henikoff, J. G · 1992
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Design by directed evolution
Arnold, F. H · 1998
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Gene ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al · 2000
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A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C · 2003
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The swiss-prot protein knowledgebase and its supplement trembl in 2003
Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O’Donovan, C., Phan, I., et al · 2003
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Swiss-prot: juggling between evolution and stability
Bairoch, A., Boeckmann, B., Ferro, S., and Gasteiger, E · 2004
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The pfam protein families database
Bateman, A., Coin, L., Durbin, R., Finn, R. D., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E. L., et al · 2004
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Uniprot archive
Leinonen, R., Diez, F. G., Binns, D., Fleischmann, W., Lopez, R., and Apweiler, R · 2004
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The universal protein resource (uniprot)
Bairoch, A., Apweiler, R., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., et al · 2005
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The iupac stability constants database
Pettit, L. D. and Powell, K · 2006
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., and Singer, Y · 2011
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The ncbi taxonomy database
Federhen, S · 2012
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Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., and Consortium, U · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Ba, J., Kiros, R., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
The coming of age of de novo protein design
Huang, P.-S., Boyken, S. E., and Baker, D · 2016
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Tying word vectors and word classifiers: A loss framework for language modeling
Inan, H., Khosravi, K., and Socher, R · 2016
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Using the output embedding to improve language models
Press, O. and Wolf, L · 2016
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Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., and Church, G. M · 2019
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
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How to hallucinate functional proteins
Costello, Z. and Martin, H. G · 2019
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Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
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Ctrl: A conditional transformer language model for controllable generation
Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., and Socher, R · 2019
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Adaptation in protein fitness landscapes is facilitated by indirect paths
Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., and Sun, R · 2016
Cited alongside, same era.
Spherical convolutions and their application in molecular modelling
Boomsma, W. and Frellsen, J · 2017
Cited alongside, same era.
Learned in translation: Contextualized word vectors
McCann, B., Bradbury, J., Xiong, C., and Socher, R · 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. u., and Polosukhin, I · 2017
Cited alongside, same era.
Generative modeling for protein structures
Anand, N. and Huang, P · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Cited alongside, same era.
Design of metalloproteins and novel protein folds using variational autoencoders
Greener, J. G., Moffat, L., and Jones, D. T · 2018
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Compressive transformers for long-range sequence modelling
Rae, J. W., Potapenko, A., Jayakumar, S. M., and Lillicrap, T. P · 2019
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Evaluating protein transfer learning with tape
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, P., Canny, J., Abbeel, P., and Song, Y · 2019
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Accelerating protein design using autoregressive generative models
Riesselman, A. J., Shin, J.-E., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., and Marks, D. S · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Goyal, S., Meier, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., and Fergus, R · 2019
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Megatron-lm: Training multi-billion parameter language models using gpu model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 2019
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A multiscale visualization of attention in the transformer model
Vig, J · 2019
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Machine learning-assisted directed protein evolution with combinatorial libraries
Wu, Z., Kan, S. J., Lewis, R. D., Wittmann, B. J., and Arnold, F. H · 2019
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Defending against neural fake news
Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y · 2019
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