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Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution.
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Structure is three to ten times more conserved than sequence—a study of structural response in protein cores
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A medicinal chemist’s guide to molecular interactions
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Deep mutational scanning: a new style of protein science
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Assessment of ligand binding site predictions in casp10
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Attention is all you need
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Deep convolutional networks for quality assessment of protein folds
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DeepSF: deep convolutional neural network for mapping protein sequences to folds
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Deep generative models of genetic variation capture the effects of mutations
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How powerful are graph neural networks?
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Axial attention in multidimensional transformers
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Generative models for graph-based protein design
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Evaluating Protein Transfer Learning with TAPE
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Learning from protein structure with geometric vector perceptrons
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Is transfer learning necessary for protein landscape prediction?
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Atom3d: Tasks on molecules in three dimensions
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Prottrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing
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Structure-aware transformer for graph representation learning
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Pifold: Toward effective and efficient protein inverse folding
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An ensemble 3d deep-learning model to predict protein metal-binding site
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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
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Lm-gvp: an extensible sequence and structure informed deep learning framework for protein property prediction
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Structure-based protein function prediction using graph convolutional networks
V. Gligorijević, P. D. Renfrew, T. Kosciolek, J. K. Leman, D. Berenberg, T. Vatanen, C. Chandler, B. C. Taylor, I. M. Fisk, H. Vlamakis, et al · 2021
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Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
P. Hermosilla, M. Schäfer, M. Lang, G. Fackelmann, P. P. Vázquez, B. Kozlíková, M. Krone, T. Ritschel, and T. Ropinski · 2021
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Highly accurate protein structure prediction with AlphaFold
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Language models enable zero-shot prediction of the effects of mutations on protein function
J. Meier, R. Rao, R. Verkuil, J. Liu, T. Sercu, and A. Rives · 2021
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Pfam: The protein families database in 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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Accurate de novo design of membrane-traversing macrocycles
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Pointsite: a point cloud segmentation tool for identification of protein ligand binding atoms
X. Yan, Y. Lu, Z. Li, Q. Wei, X. Gao, S. Wang, S. Wu, and S. Cui · 2022
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Protein representation learning by geometric structure pretraining
Z. Zhang, M. Xu, A. R. Jamasb, V. Chenthamarakshan, A. Lozano, P. Das, and J. Tang · 2022
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xtrimopglm: Unified 100b-scale pre-trained transformer for deciphering the language of protein
B. Chen, X. Cheng, Y. ao Geng, S. Li, X. Zeng, B. Wang, J. Gong, C. Liu, A. Zeng, Y. Dong, J. Tang, and L. Song · 2023
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Ankh: Optimized protein language model unlocks general-purpose modelling
A. Elnaggar, H. Essam, W. Salah-Eldin, W. Moustafa, M. Elkerdawy, C. Rochereau, and B. Rost · 2023
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Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
A. Kroll, Y. Rousset, X.-P. Hu, N. A. Liebrand, and M. J. Lercher · 2023
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ProteinShake – Building datasets and benchmarks for deep learning on protein structures
T. Kucera, C. Oliver, D. Chen, and K. Borgwardt · 2023
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Alphafold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel cdk20 small molecule inhibitor
F. Ren, X. Ding, M. Zheng, M. Korzinkin, X. Cai, W. Zhu, A. Mantsyzov, A. Aliper, V. Aladinskiy, Z. Cao, et al · 2023
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Structure-informed language models are protein designers
Z. Zheng, Y. Deng, D. Xue, Y. Zhou, F. Ye, and Q. Gu · 2023
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