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The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences.
Design by Directed Evolution
Arnold, F. H · 1998
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
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Fast differentiable dna and protein sequence optimization for molecular design (2020)
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Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes
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Adaptation in protein fitness landscapes is facilitated by indirect paths
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Phate: a dimensionality reduction method for visualizing trajectory structures in high-dimensional biological data
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Attention is all you need
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Spectral norm regularization for improving the generalizability of deep learning
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R. et al · 2018
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Conditioning by adaptive sampling for robust design
Brookes, D., Park, H. & Listgarten, J · 2019
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Machine-learning-guided directed evolution for protein engineering
Yang, K. K., Wu, Z. & Arnold, F. H · 2019
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Model-based reinforcement learning for biological sequence design
Transformer protein language models are unsupervised structure learners
Rao, R., Ovchinnikov, S., Meier, J., Rives, A. & Sercu, T · 2020
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Bertology meets biology: Interpreting attention in protein language models
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Uncovering the folding landscape of rna secondary structure using deep graph embeddings
Castro, E., Benz, A., Tong, A., Wolf, G. & Krishnaswamy, S · 2020
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How “bertology” changed the state-of-the-art also for italian nlp
Tamburini, F · 2020
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Protein sequence design by conformational landscape optimization
Norn, C. et al · 2021
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Low-n protein engineering with data-efficient deep learning
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Evaluating protein transfer learning with tape
Rao, R. et al · 2019
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Engineering new catalytic activities in enzymes
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Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M. & Church, G. M · 2021
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Highly accurate protein structure prediction with alphafold
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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Pfam: The protein families database in 2021
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