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Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized.
Description of organic reactions based on imaginary transition structures. 1. introduction of new concepts
Fujita, S · 1986
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
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Theoretical insights in enzyme catalysis
Martí, S., Roca, M., Andrés, J., Moliner, V., Silla, E., Tuñón, I., and Bertrán, J · 2004
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Selection and evolution of enzymes from a partially randomized non-catalytic scaffold
Seelig, B. and Szostak, J. W · 2007
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Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem
Truchon, J.-F. and Bayly, C. I · 2007
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De novo computational design of retro-aldol enzymes
Jiang, L., Althoff, E. A., Clemente, F. R., Doyle, L., Rothlisberger, D., Zanghellini, A., Gallaher, J. L., Betker, J. L., Tanaka, F., Barbas III, C. F., et al · 2008
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Kemp elimination catalysts by computational enzyme design
Röthlisberger, D., Khersonsky, O., Wollacott, A. M., Jiang, L., DeChancie, J., Betker, J., Gallaher, J. L., Althoff, E. A., Zanghellini, A., Dym, O., et al · 2008
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Condensed graph of reaction: considering a chemical reaction as one single pseudo molecule
Hoonakker, F., Lachiche, N., Varnek, A., and Wagner, A · 2011
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Engineering the third wave of biocatalysis
Bornscheuer, U. T., Huisman, G., Kazlauskas, R., Lutz, S., Moore, J., and Robins, K · 2012
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Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment
Remmert, M., Biegert, A., Hauser, A., and Söding, J · 2012
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Structure-reactivity relationships in terms of the condensed graphs of reactions
Madzhidov, T., Polishchuk, P., Nugmanov, R., Bodrov, A., Lin, A., Baskin, I., Varnek, A., and Antipin, I · 2014
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Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., and Consortium, U · 2015
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Improved deep metric learning with multi-class n-pair loss objective
Sohn, K · 2016
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Predicting organic reaction outcomes with weisfeiler-lehman network
Jin, W., Coley, C., Barzilay, R., and Jaakkola, T · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Uniclust databases of clustered and deeply annotated protein sequences and alignments
Mirdita, M., Von Den Driesch, L., Galiez, C., Martin, M. J., Söding, J., and Steinegger, M · 2017
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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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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PyTorch Lightning, March 2019
Falcon, W. and The PyTorch Lightning team · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 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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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers
Ryu, J. Y., Kim, H. U., and Lee, S. Y · 2019
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Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A · 2019
Cited alongside, same era.
A computational method for design of connected catalytic networks in proteins
Weitzner, B. D., Kipnis, Y., Daniel, A. G., Hilvert, D., and Baker, D · 2019
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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., Smetanin, N., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al · 2022
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Colabfold: making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., and Steinegger, M · 2022
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Design of peptide-based protein degraders via contrastive deep learning. biorxiv 2022
Palepu, K., Ponnapati, M., Bhat, S., Tysinger, E., Stan, T., Brixi, G., Koseki, S., and Chatterjee, P · 2022
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Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al · 2022
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Nucleic Acids Research , 51(D1):D523–D531, 2023
Uniprot: the universal protein knowledgebase in 2023 · 2023
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Analyzing learned molecular representations for property prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al · 2019
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
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Brenda, the elixir core data resource in 2021: new developments and updates
Chang, A., Jeske, L., Ulbrich, S., Hofmann, J., Koblitz, J., Schomburg, I., Neumann-Schaal, M., Jahn, D., and Schomburg, D · 2021
Cited alongside, same era.
Machine learning for enzyme engineering, selection and design
Feehan, R., Montezano, D., and Slusky, J. S · 2021
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Machine learning of reaction properties via learned representations of the condensed graph of reaction
Heid, E. and Green, W. H · 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., et al · 2021
Cited alongside, same era.
Large-scale screening reveals that geometric structure matters more than electronic structure in the bioinspired catalyst design of formate dehydrogenase mimics
Liu, M., Nazemi, A., Taylor, M. G., Nandy, A., Duan, C., Steeves, A. H., and Kulik, H. J · 2021
Cited alongside, same era.
Alphafold2 and deep learning for elucidating enzyme conformational flexibility and its application for design
Casadevall, G., Duran, C., and Osuna, S · 2023
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Duan, C., Du, Y., Jia, H., and Kulik, H. J · 2023
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Enzymemap: Curation, validation and data-driven prediction of enzymatic reactions
Heid, E., Probst, D., Green, W. H., and Madsen, G. K · 2023
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Building enzymes through design and evolution
Hossack, E. J., Hardy, F. J., and Green, A. P · 2023
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Enzyme function and evolution through the lens of bioinformatics
Ribeiro, A. J., Riziotis, I. G., Borkakoti, N., and Thornton, J. M · 2023
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Proteinfer, deep neural networks for protein functional inference
Sanderson, T., Bileschi, M. L., Belanger, D., and Colwell, L. J · 2023
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Contrastive learning in protein language space predicts interactions between drugs and protein targets
Singh, R., Sledzieski, S., Bryson, B., Cowen, L., and Berger, B · 2023
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Equireact: An equivariant neural network for chemical reactions
van Gerwen, P., Briling, K. R., Bunne, C., Somnath, V. R., Laplaza, R., Krause, A., and Corminboeuf, C · 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., 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., et al · 2023
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De novo design of luciferases using deep learning
Yeh, A. H.-W., Norn, C., Kipnis, Y., Tischer, D., Pellock, S. J., Evans, D., Ma, P., Lee, G. R., Zhang, J. Z., Anishchenko, I., et al · 2023
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Enzyme function prediction using contrastive learning
Yu, T., Cui, H., Li, J. C., Luo, Y., Jiang, G., and Zhao, H · 2023
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Discovery and characterization of terpene synthases powered by machine learning
Samusevich, R., Hebra, T., Bushuiev, R., Bushuiev, A., Chatpatanasiri, R., Kulhánek, J., Čalounová, T., Perković, M., Engst, M., Tajovská, A., Sivic, J., and Pluskal, T · 2024
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Directed evolution of enzymatic silicon-carbon bond cleavage in siloxanes
Sarai, N. S., Fulton, T. J., O’Meara, R. L., Johnston, K. E., Brinkmann-Chen, S., Maar, R. R., Tecklenburg, R. E., Roberts, J. M., Reddel, J. C. T., Katsoulis, D. E., and Arnold, F. H · 2024
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