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This paper demonstrates that language models are strong structure-based protein designers.
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When a domain isn’ta domain, and why it’s important to properly filter proteins in databases: Conflicting definitions and fold classification systems for structural domains makes filtering of such databases imperative
Towse, C.-L. and Daggett, V · 2012
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Auto-encoding variational bayes
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Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
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Neural machine translation by jointly learning to align and translate
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The coming of age of de novo protein design
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The rosetta all-atom energy function for macromolecular modeling and design
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
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Attention is all you need
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Rosettaantibodydesign (rabd): A general framework for computational antibody design
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Non-autoregressive neural machine translation
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Mask-predict: Parallel decoding of conditional masked language models
Ghazvininejad, M., Levy, O., Liu, Y., and Zettlemoyer, L · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 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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What would elsa do? freezing layers during transformer fine-tuning
Adversarial contrastive pre-training for protein sequences
McDermott, M., Yap, B., Hsu, H., Jin, D., and Szolovits, P · 2021
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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
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Pre-training of deep bidirectional protein sequence representations with structural information
Min, S., Park, S., Kim, S., Choi, H.-S., Lee, B., and Yoon, S · 2021
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Tripletprot: deep representation learning of proteins based on siamese networks
Nourani, E., Asgari, E., McHardy, A. C., and Mofrad, M. R · 2021
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Step-unrolled denoising autoencoders for text generation
Savinov, N., Chung, J., Binkowski, M., Elsen, E., and van den Oord, A · 2021
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Lee, J., Tang, R., and Lin, J · 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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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., and Fergus, R · 2019
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Machine-learning-guided directed evolution for protein engineering
Yang, K. K., Wu, Z., and Arnold, F. H · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J. L., and Dror, R · 2020
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Su, J., Lu, Y., Pan, S., Murtadha, A., Wen, B., and Liu, Y · 2021
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On memorization in probabilistic deep generative models
van den Burg, G. and Williams, C · 2021
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Modeling protein using large-scale pretrain language model
Xiao, Y., Qiu, J., Li, Z., Hsieh, C.-Y., and Tang, J · 2021
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Proteinbert: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., and Linial, M · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I., Courbet, A., de Haas, R. J., Bethel, N., et al · 2022
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Controllable protein design with language models
Ferruz, N. and Höcker, B · 2022
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Protgpt2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., and Höcker, B · 2022
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Complexity-based prompting for multi-step reasoning
Fu, Y., Peng, H., Sabharwal, A., Clark, P., and Khot, T · 2022
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Pifold: Toward effective and efficient protein inverse folding
Gao, Z., Tan, C., and Li, S. Z · 2022
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Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
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Exploring evolution-aware &-free protein language models as protein function predictors
Hu, M., Yuan, F., Yang, K. K., Ju, F., Su, J., Wang, H., Yang, F., and Ding, Q · 2022
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Conditional antibody design as 3d equivariant graph translation
Kong, X., Huang, W., and Liu, Y · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al · 2022
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Ablang: An antibody language model for completing antibody sequences
Olsen, T. H., Moal, I. H., and Deane, C. M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Generative de novo protein design with global context
Tan, C., Gao, Z., Xia, J., and Li, S. Z · 2022
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Learning functional properties of proteins with language models
Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., and Doğan, T · 2022
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Language models generalize beyond natural proteins
Verkuil, R., Kabeli, O., Du, Y., Wicky, B. I., Milles, L. F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T., and Rives, A · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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Convolutions are competitive with transformers for protein sequence pretraining
Yang, K. K., Lu, A. X., and Fusi, N · 2022
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Masked inverse folding with sequence transfer for protein representation learning
Yang, K. K., Zanichelli, N., and Yeh, H · 2022
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