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Protein sequence design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored.
Pseudolikelihood reranking with masked language models
Salazar, J., Liang, D., Nguyen, T. Q., and Kirchhoff, K · 1910
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Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Kabsch, W. and Sander, C · 1983
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Scop: a structural classification of proteins database for the investigation of sequences and structures
Murzin, A. G., Brenner, S. E., Hubbard, T., and Chothia, C · 1995
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Scoring function for automated assessment of protein structure template quality
Zhang, Y. and Skolnick, J · 2004
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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Interproscan 5: genome-scale protein function classification
Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., et al · 2014
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The superfamily 1.75 database in 2014: a doubling of data
Oates, M. E., Stahlhacke, J., Vavoulis, D. V., Smithers, B., Rackham, O. J., Sardar, A. J., Zaucha, J., Thurlby, N., Fang, H., and Gough, J · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Functional roles of transiently and intrinsically disordered regions within proteins
Uversky, V. N · 2015
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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
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Mobidb 3.0: more annotations for intrinsic disorder, conformational diversity and interactions in proteins
Piovesan, D., Tabaro, F., Paladin, L., Necci, M., Mičetić, I., Camilloni, C., Davey, N., Dosztányi, Z., Mészáros, B., Monzon, A. M., et al · 2018
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
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Uniprot: the universal protein knowledgebase in 2021
Consortium, T. U · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Planner: Generating diversified paragraph via latent language diffusion model
Zhang, Y., Gu, J., Wu, Z., Zhai, S., Susskind, J., and Jaitly, N · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., Bhowmik, D., and Rost, B · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Structure-based protein design with deep learning
Ovchinnikov, S. and Huang, P.-S · 2021
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Grad-tts: A diffusion probabilistic model for text-to-speech
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., and Kudinov, M · 2021
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Expanding functional protein sequence spaces using generative adversarial networks
Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., Savolainen, O., Meskys, R., Engqvist, M. K. M., and Zelezniak, A · 2021
Earlier work this paper cites.
Alphafold and implications for intrinsically disordered proteins
Ruff, K. M. and Pappu, R. V · 2021
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Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., Velankar, S., Kleywegt, G. J., Bateman, A., Evans, R., Pritzel, A., Figurnov, M., Ronneberger, O., Bates, R., Kohl, S. A. A., Potapenko, A., Ballard, A. J., Romera-Paredes, B., Nikolov, S., Jain, R., Clancy, E., Reiman, D., Petersen, S., Senior, A. W., Kavukcuoglu, K., Birney, E., Kohli, P., Jumper, J., and Hassabis, D · 2021
Cited alongside, same era.
Protein sequence design with deep generative models
Wu, Z., Johnston, K. E., Arnold, F. H., and Yang, K. K · 2021
Cited alongside, same era.
Learning inverse folding from millions of predicted structures , volume 162 of Proceedings of Machine Learning Research , 17–23 Jul 2022. PMLR
Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S. (eds.) · 2022
Cited alongside, same era.
Analog bits: Generating discrete data using diffusion models with self-conditioning
Chen, T., Zhang, R., and Hinton, G · 2022
Cited alongside, same era.
Lin, Y. and AlQuraishi, M · 2023
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Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser, J. S., and Naik, N · 2023
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Tess: Text-to-text self-conditioned simplex diffusion
Mahabadi, R. K., Ivison, H., Tae, J., Henderson, J., Beltagy, I., Peters, M. E., and Cohan, A · 2023
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Interpro in 2022
Paysan-Lafosse, T., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Bork, P., Bridge, A., Colwell, L., et al · 2023
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Intrinsic structural dynamics dictate enzymatic activity and inhibition
Shukla, V. K., Siemons, L., and Hansen, D. F · 2023
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Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., De Haas, R. J., Bethel, N., Leung, P. J. Y., Huddy, T. F., Pellock, S., Tischer, D., Chan, F., Koepnick, B., Nguyen, H., Kang, A., Sankaran, B., Bera, A. K., King, N. P., and Baker, D · 2022
Cited alongside, same era.
Difformer: Empowering diffusion model on embedding space for text generation
Gao, Z., Guo, J., Tan, X., Zhu, Y., Zhang, F., Bian, J., and Xu, L · 2022
Cited alongside, same era.
Diffuseq: Sequence to sequence text generation with diffusion models
Gong, S., Li, M., Feng, J., Wu, Z., and Kong, L · 2022
Cited alongside, same era.
Han, X., Kumar, S., and Tsvetkov, Y · 2022
Cited alongside, same era.
Rita: a study on scaling up generative protein sequence models
Hesslow, D., Zanichelli, N., Notin, P., Poli, I., and Marks, D · 2022
Cited alongside, same era.
Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S., and Hashimoto, T. B · 2022
Cited alongside, same era.
Latent diffusion for language generation
Lovelace, J., Kishore, V., Wan, C., Shekhtman, E., and Weinberger, K · 2022
Cited alongside, same era.
Self-conditioned embedding diffusion for text generation
Strudel, R., Tallec, C., Altché, F., Du, Y., Ganin, Y., Mensch, A., Grathwohl, W., Savinov, N., Dieleman, S., Sifre, L., et al · 2022
Cited alongside, same era.
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Saprot: Protein language modeling with structure-aware vocabulary
Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X., and Yuan, F · 2023
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Fast and accurate protein structure search with Foldseek
Van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., and Steinegger, M · 2023
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De novo design of protein structure and function with RFdiffusion
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., Wicky, B. I. M., Hanikel, N., Pellock, S. J., Courbet, A., Sheffler, W., Wang, J., Venkatesh, P., Sappington, I., Torres, S. V., Lauko, A., De Bortoli, V., Mathieu, E., Ovchinnikov, S., Barzilay, R., Jaakkola, T. S., DiMaio, F., Baek, M., and Baker, D · 2023
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Dinoiser: Diffused conditional sequence learning by manipulating noises
Ye, J., Zheng, Z., Bao, Y., Qian, L., and Wang, M · 2023
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Pro-ldm: Protein sequence generation with a conditional latent diffusion model
Zhang, S., Jiang, Z., Huang, R., Mo, S., Zhu, L., Li, P., Zhang, Z., Pan, E., Chen, X., Long, Y., et al · 2023
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Campbell, A., Yim, J., Barzilay, R., Rainforth, T., and Jaakkola, T · 2024
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A latent diffusion model for protein structure generation
Fu, C., Yan, K., Wang, L., Au, W. Y., McThrow, M. C., Komikado, T., Maruhashi, K., Uchino, K., Qian, X., and Ji, S · 2024
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Learning the language of protein structure
Gaujac, B., Donà, J., Copoiu, L., Atkinson, T., Pierrot, T., and Barrett, T. D · 2024
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Sequence-augmented se(3)-flow matching for conditional protein backbone generation, 2024
Huguet, G., Vuckovic, J., Fatras, K., Thibodeau-Laufer, E., Lemos, P., Islam, R., Liu, C.-H., Rector-Brooks, J., Akhound-Sadegh, T., Bronstein, M., Tong, A., and Bose, A. J · 2024
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Nv-embed: Improved techniques for training llms as generalist embedding models
Lee, C., Roy, R., Xu, M., Raiman, J., Shoeybi, M., Catanzaro, B., and Ping, W · 2024
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Lin, Y., Lee, M., Zhang, Z., and AlQuraishi, M · 2024
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Tokenized and continuous embedding compressions of protein sequence and structure
Lu, A. X., Yan, W., Yang, K. K., Gligorijevic, V., Cho, K., Abbeel, P., Bonneau, R., and Frey, N · 2024
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Prollama: A protein large language model for multi-task protein language processing
Lv, L., Lin, Z., Li, H., Liu, Y., Cui, J., Chen, C. Y.-C., Yuan, L., and Tian, Y · 2024
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Esm cambrian: Revealing the mysteries of proteins with unsupervised learning
Team, E · 2024
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Diffusion language models are versatile protein learners
Wang, X., Zheng, Z., Ye, F., Xue, D., Huang, S., and Gu, Q · 2024
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Improved motif-scaffolding with SE(3) flow matching
Yim, J., Campbell, A., Mathieu, E., Foong, A. Y. K., Gastegger, M., Jimenez-Luna, J., Lewis, S., Satorras, V. G., Veeling, B. S., Noe, F., Barzilay, R., and Jaakkola, T · 2024
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DPLM-2: A multimodal diffusion protein language model
Anonymous · 2025
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Interpro: the protein sequence classification resource in 2025¡? mode longmeta?¿
Blum, M., Andreeva, A., Florentino, L. C., Chuguransky, S. R., Grego, T., Hobbs, E., Pinto, B. L., Orr, A., Paysan-Lafosse, T., Ponamareva, I., et al · 2025
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ProtGPT2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., and Höcker, B · 2041
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Protein design and variant prediction using autoregressive generative models
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., and Marks, D. S · 2041
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