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Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Kabsch, W. & Sander, C · 1983
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Long short-term memory
Hochreiter, S. & Schmidhuber, J · 1997
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Gene ontology: tool for the unification of biology
Ashburner, M. et al · 2000
Earlier work this paper cites.
The protein data bank
Berman, H. M. et al · 2000
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Protein structure prediction using rosetta
Rohl, C. A., Strauss, C. E., Misura, K. M. & Baker, D · 2004
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M. & Huang, F · 2006
Earlier work this paper cites.
Nucleic Acids Research 36
The universal protein resource (uniprot) · 2007
Earlier work this paper cites.
Uniprotkb/swiss-prot
Boutet, E., Lieberherr, D., Tognolli, M., Schneider, M. & Bairoch, A · 2007
Earlier work this paper cites.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Chaudhury, S., Lyskov, S. & Gray, J. J · 2010
Earlier work this paper cites.
Design of o-acetylserine sulfhydrylase inhibitors by mimicking nature
Salsi, E. et al · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Introduction to protein structure (Garland Science, 2012)
Branden, C. I. & Tooze, J · 2012
Earlier work this paper cites.
Scope: Structural classification of proteins—extended, integrating scop and astral data and classification of new structures
Fox, N. K., Brenner, S. E. & Chandonia, J.-M · 2013
Earlier work this paper cites.
Neuropid: a predictor for identifying neuropeptide precursors from metazoan proteomes
Ofer, D. & Linial, M · 2014
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Simultaneous optimization of biomolecular energy functions on features from small molecules and macromolecules
Park, H. et al · 2016
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Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S. et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Asap: a machine learning framework for local protein properties
Brandes, N., Ofer, D. & Linial, M · 2016
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Attention is all you need
Vaswani, A. et al · 2017
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Global analysis of protein folding using massively parallel design, synthesis, and testing
Rocklin, G. J. et al · 2017
Earlier work this paper cites.
Deeploc: prediction of protein subcellular localization using deep learning
Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H. & Winther, O · 2017
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Clustering huge protein sequence sets in linear time
Steinegger, M. & Söding, J · 2018
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Semantic scholar
Fricke, S · 2018
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Deepsf: deep convolutional neural network for mapping protein sequences to folds
Hou, J., Adhikari, B. & Cheng, J · 2018
Earlier work this paper cites.
Critical assessment of methods of protein structure prediction (casp)-round xii
Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T. & Tramontano, A · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y. & Vinyals, O · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K · 2019
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Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Steinegger, M., Mirdita, M. & Söding, J · 2019
Earlier work this paper cites.
Scibert: A pretrained language model for scientific text
Beltagy, I., Lo, K. & Cohan, A · 2019
Earlier work this paper cites.
Evaluating protein transfer learning with tape
Rao, R. et al · 2019
Earlier work this paper cites.
Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning
Klausen, M. S. et al · 2019
Earlier work this paper cites.
Proteinnet: a standardized data set for machine learning of protein structure
AlQuraishi, M · 2019
Earlier work this paper cites.
Pubtator central: automated concept annotation for biomedical full text articles
Wei, C.-H., Allot, A., Leaman, R. & Lu, Z · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A. et al · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Song, Y. & Ermon, S · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D. et al · 2019
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D. & Buchwalter, W · 2019
Earlier work this paper cites.
A comparative study on transformer vs RNN in speech applications
Karita, S. et al · 2019
Earlier work this paper cites.
How to hallucinate functional proteins
Costello, Z. & Martin, H. G · 2019
Earlier work this paper cites.
Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R. & Jaakkola, T · 2019
Earlier work this paper cites.
Signalp 5.0 improves signal peptide predictions using deep neural networks
Almagro Armenteros, J. J. et al · 2019
Earlier work this paper cites.
Model-based reinforcement learning for biological sequence design
Angermueller, C. et al · 2020
Earlier work this paper cites.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M. et al · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C. et al · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A. & Abbeel, P · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R · 2020
Cited alongside, same era.
Supervised contrastive learning
Khosla, P. et al · 2020
Cited alongside, same era.
Voice transformer network: Sequence-to-sequence voice conversion using transformer with text-to-speech pretraining
Huang, W., Hayashi, T., Wu, Y., Kameoka, H. & Toda, T · 2020
Cited alongside, same era.
Structured multi-view representations for drug combinations
Liu, S., Deac, A., Zhu, Z. & Tang, J · 2020
Cited alongside, same era.
A generative neural network for maximizing fitness and diversity of synthetic DNA and protein sequences
Linder, J., Bogard, N., Rosenberg, A. B. & Seelig, G · 2020
Ontoprotein: Protein pretraining with gene ontology embedding
Zhang, N. et al · 2022
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Tranception: Protein fitness prediction with autoregressive transformers and inference-time retrieval
Notin, P. et al · 2022
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Pre-training molecular graph representation with 3d geometry
Liu, S. et al · 2022
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Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S. & Hashimoto, T. B · 2022
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Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes
Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T. P. & Willcocks, C. G · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
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Cited alongside, same era.
De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks
Karimi, M., Zhu, S., Cao, Y. & Shen, Y · 2020
Cited alongside, same era.
Designing a prospective COVID-19 therapeutic with reinforcement learning
Skwark, M. J. et al · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M. & Hinton, G. E · 2020
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
Jumper, J. et al · 2021
Cited alongside, same era.
Cryodrgn2: Ab initio neural reconstruction of 3d protein structures from real cryo-em images
Zhong, E. D., Lerer, A., Davis, J. H. & Berger, B · 2021
Cited alongside, same era.
Saharia, C. et al · 2022
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A molecular multimodal foundation model associating molecule graphs with natural language
Su, B. et al · 2022
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Protgpt2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S. & Höcker, B · 2022
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Transformer-based protein generation with regularized latent space optimization
Castro, E. et al · 2022
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Designing biological sequences via meta-reinforcement learning and Bayesian optimization
Feng, L., Nouri, P., Muni, A., Bengio, Y. & Bacon, P.-L · 2022
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Protein Sequence Design in a Latent Space via Model-based Reinforcement Learning
Lee, M. et al · 2022
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Protein design via deep learning
Ding, W., Nakai, K. & Gong, H · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Dauparas, J. et al · 2022
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Anand, N. & Achim, T · 2022
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A high-level programming language for generative protein design
Hie, B. et al · 2022
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Language models generalize beyond natural proteins
Verkuil, R. et al · 2022
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Stylet2i: Toward compositional and high-fidelity text-to-image synthesis
Li, Z., Min, M. R., Li, K. & Xu, C · 2022
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Proteinbert: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N. & Linial, M · 2022
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Sesnet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Li, M. et al · 2023
Closest in time.
Learning protein representations via complete 3d graph networks
Wang, L., Liu, H., Liu, Y., Kurtin, J. & Ji, S · 2023
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Multi-modal molecule structure–text model for text-based retrieval and editing
Liu, S. et al · 2023
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Uniprotkg/swiss-prot (2023)
UniProt · 2023
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Chatpathway: Conversational large language models for biology pathway detection
Li, Y., Xu, H., Zhao, H., Guo, H. & Liu, S · 2023
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Drug discovery companies are customizing chatgpt: here’s how
Savage, N · 2023
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Text generation with diffusion language models: A pre-training approach with continuous paragraph denoise
Lin, Z. et al · 2023
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Illuminating protein space with a programmable generative model
Ingraham, J. et al · 2023
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Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Liu, S., Guo, H. & Tang, J · 2023
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Muse: Text-to-image generation via masked generative transformers
Chang, H. et al · 2023
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Disentangled wasserstein autoencoder for t-cell receptor engineering
Li, T., Guo, H., Grazioli, F., Gerstein, M. & Min, M. R · 2023
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Prot-vae: Protein transformer variational autoencoder for functional protein design
Sevgen, E. et al · 2023
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Generative power of a protein language model trained on multiple sequence alignments
Sgarbossa, D., Lupo, U. & Bitbol, A.-F · 2023
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Binding peptide generation for mhc class i proteins with deep reinforcement learning
Chen, Z. et al · 2023
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T-cell receptor optimization with reinforcement learning and mutation polices for precision immunotherapy
Chen, Z. et al · 2023
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Structure-informed language models are protein designers
Zheng, Z. et al · 2023
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Score-based generative modeling for de novo protein design
Lee, J. S., Kim, J. & Kim, P. M · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L. et al · 2023
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Continuous-discrete convolution for geometry-sequence modeling in proteins
Fan, H., Wang, Z., Yang, Y. & Kankanhalli, M · 2023
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Symmetry-informed geometric representation for molecules, proteins, and crystalline materials
Liu, S. et al · 2023
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Conversational drug editing using retrieval and domain feedback
Liu, S. et al · 2024
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Empowering diffusion models on the embedding space for text generation
Gao, Z. et al · 2024
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Lumiere: A space-time diffusion model for video generation
Bar-Tal, O. et al · 2024
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A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics
Liu, S. et al · 2024
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A text-guided protein design framework
Liu, S. et al · 2025
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