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Enzymes are important proteins that catalyze chemical reactions.
SciBERT: A Pretrained Language Model for Scientific Text, September 2019
Iz Beltagy, Kyle Lo, and Arman Cohan · 1903
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Basic local alignment search tool
Stephen F AltschuP, Warren Gish, Webb Miller, Eugene W Myers, and David J Lipman · 1990
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Twilight zone of protein sequence alignments
Burkhard Rost · 1999
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Gene Ontology: tool for the unification of biology
Michael Ashburner, Catherine A. Ball, Judith A. Blake, David Botstein, Heather Butler, J. Michael Cherry, Allan P. Davis, Kara Dolinski, Selina S. Dwight, Janan T. Eppig, Midori A. Harris, David P. Hill, Laurie Issel-Tarver, Andrew Kasarskis, Suzanna Lewis, John C. Matese, Joel E. Richardson, Martin Ringwald, Gerald M. Rubin, and Gavin Sherlock · 2000
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Supervised Contrastive Learning, March 2021
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2004
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Exploring protein fitness landscapes by directed evolution
Philip A Romero and Frances H Arnold · 2009
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Enzyme Promiscuity: A Mechanistic and Evolutionary Perspective
Olga Khersonsky and Dan S. Tawfik · 2010
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HMMER web server: 2015 update
Robert D. Finn, Jody Clements, William Arndt, Benjamin L. Miller, Travis J. Wheeler, Fabian Schreiber, Alex Bateman, and Sean R. Eddy · 2015
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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Directed Evolution: Bringing New Chemistry to Life
Frances H. Arnold · 2018
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Mutant phenotypes for thousands of bacterial genes of unknown function
Morgan N. Price, Kelly M. Wetmore, R. Jordan Waters, Mark Callaghan, Jayashree Ray, Hualan Liu, Jennifer V. Kuehl, Ryan A. Melnyk, Jacob S. Lamson, Yumi Suh, Hans K. Carlson, Zuelma Esquivel, Harini Sadeeshkumar, Romy Chakraborty, Grant M. Zane, Benjamin E. Rubin, Judy D. Wall, Axel Visel, James Bristow, Matthew J. Blow, Adam P. Arkin, and Adam M. Deutschbauer · 2018
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Selenzyme: enzyme selection tool for pathway design
Pablo Carbonell, Jerry Wong, Neil Swainston, Eriko Takano, Nicholas J Turner, Nigel S Scrutton, Douglas B Kell, Rainer Breitling, and Jean-Loup Faulon · 2018
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Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee · 2019
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Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Xi Chen, John Canny, Pieter Abbeel, and Yun S. Song · 2019
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Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A. Hunter, Costas Bekas, and Alpha A. Lee · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Engineering new catalytic activities in enzymes
Kai Chen and Frances H. Arnold · 2020
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Pfam: The protein families database in 2021
Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A Salazar, Erik L L Sonnhammer, Silvio C E Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, Robert D Finn, and Alex Bateman · 2021
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BRENDA, the ELIXIR core data resource in 2021: new developments and updates
Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, and Dietmar Schomburg · 2021
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RetroBioCat as a computer-aided synthesis planning tool for biocatalytic reactions and cascades
William Finnigan, Lorna J. Hepworth, Sabine L. Flitsch, and Nicholas J. Turner · 2021
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FLIP: Benchmark tasks in fitness landscape inference for proteins
Christian Dallago, Jody Mou, Kadina E. Johnston, Bruce J. Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, and Kevin K. Yang · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
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ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, and Burkhard Rost · 2021
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Mapping the space of chemical reactions using attention-based neural networks
Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, and Jean-Louis Reymond · 2021
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Fast and accurate protein structure search with Foldseek
Michel Van Kempen, Stephanie S. Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron L.M. Gilchrist, Johannes Söding, and Martin Steinegger · 2022
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Using deep learning to annotate the protein universe
Maxwell L. Bileschi, David Belanger, Drew H. Bryant, Theo Sanderson, Brandon Carter, D. Sculley, Alex Bateman, Mark A. DePristo, and Lucy J. Colwell · 2022
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Rhea, the reaction knowledgebase in 2022
Parit Bansal, Anne Morgat, Kristian B Axelsen, Venkatesh Muthukrishnan, Elisabeth Coudert, Lucila Aimo, Nevila Hyka-Nouspikel, Elisabeth Gasteiger, Arnaud Kerhornou, Teresa Batista Neto, Monica Pozzato, Marie-Claude Blatter, Alex Ignatchenko, Nicole Redaschi, and Alan Bridge · 2022
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Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation, January 2022
Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli, and Pierre Baldi · 2022
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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2022
Cited alongside, same era.
From nature to industry: Harnessing enzymes for biocatalysis
R. Buller, S. Lutz, R. J. Kazlauskas, R. Snajdrova, J. C. Moore, and U. T. Bornscheuer · 2023
Cited alongside, same era.
Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, Anton Bushuiev, Raman Samusevich, Jiri Sedlar, Jiri Damborsky, Tomas Pluskal, Josef Sivic, and Stanislav Mazurenko · 2023
Cited alongside, same era.
Machine Learning for Protein Engineering, May 2023
Kadina E. Johnston, Clara Fannjiang, Bruce J. Wittmann, Brian L. Hie, Kevin K. Yang, and Zachary Wu · 2023
Cited alongside, same era.
Functional profiling of the sequence stockpile: a review and assessment of in silico prediction tools, July 2023
Prabakaran Ramakrishnan and Yana Bromberg · 2023
Cited alongside, same era.
ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning
Jin Su, Xibin Zhou, Xuting Zhang, and Fajie Yuan · 2024
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VIPER: A General Model for Prediction of Enzyme Substrates
Max James Campbell · 2024
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Generation of connections between protein sequence space and chemical space to enable a predictive model for biocatalysis
Alexandra Paton, Daniil Boiko, Jonathan Perkins, Nicholas Cemalovic, Thiago Reschützegger, Gabe Gomes, and Alison Narayan · 2024
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Scientific Large Language Models: A Survey on Biological & Chemical Domains, January 2024
Qiang Zhang, Keyang Ding, Tianwen Lyv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Mengyao Zhang, Jinlu Zhang, Jiyu Cui, Renjun Xu, Hongyang Chen, Xiaohui Fan, Huabin Xing, and Huajun Chen · 2024
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A Review of Large Language Models and Autonomous Agents in Chemistry, June 2024
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Kegg for taxonomy-based analysis of pathways and genomes
Minoru Kanehisa, Miho Furumichi, Yoko Sato, Masayuki Kawashima, and Mari Ishiguro-Watanabe · 2023
Cited alongside, same era.
RetroBioCat Database: A Platform for Collaborative Curation and Automated Meta-Analysis of Biocatalysis Data
William Finnigan, Max Lubberink, Lorna J. Hepworth, Joan Citoler, Ashley P. Mattey, Grayson J. Ford, Jack Sangster, Sebastian C. Cosgrove, Bruna Zucoloto Da Costa, Rachel S. Heath, Thomas W. Thorpe, Yuqi Yu, Sabine L. Flitsch, and Nicholas J. Turner · 2023
Cited alongside, same era.
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Pascal Notin, Aaron W Kollasch, Daniel Ritter, Lood van Niekerk, Steffanie Paul, Hansen Spinner, Nathan Rollins, Ada Shaw, Ruben Weitzman, Jonathan Frazer, Mafalda Dias, Dinko Franceschi, Rose Orenbuch, Yarin Gal, and Debora S Marks · 2023
Cited alongside, same era.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, and Alexander Rives · 2023
Cited alongside, same era.
Contrasting Sequence with Structure: Pre-training Graph Representations with PLMs
Louis Callum Butler Robinson, Timothy Atkinson, Liviu Copoiu, Patrick Bordes, Thomas Pierrot, and Thomas Barrett · 2023
Cited alongside, same era.
Learning sequence, structure, and function representations of proteins with language models
Tymor Hamamsy, Meet Barot, James T. Morton, Martin Steinegger, Richard Bonneau, and Kyunghyun Cho · 2023
Cited alongside, same era.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction, October 2023
Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, and Huajun Chen · 2023
Cited alongside, same era.
Mayk Caldas Ramos, Christopher J. Collison, and Andrew D. White · 2024
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Eli M. Carrami and Sahand Sharifzadeh · 2024
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InstructBioMol: Advancing Biomolecule Understanding and Design Following Human Instructions
Xiang Zhuang, Keyan Ding, Tianwen Lyu, Yinuo Jiang, Xiaotong Li, Zhuoyi Xiang, Zeyuan Wang, Ming Qin, Kehua Feng, Jike Wang, Qiang Zhang, and Huajun Chen · 2024
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ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
Yijia Xiao, Edward Sun, Yiqiao Jin, Qifan Wang, and Wei Wang · 2024
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Multi-Modal Large Language Model Enables Protein Function Prediction
Mingjia Huo, Han Guo, Xingyi Cheng, Digvijay Singh, Hamidreza Rahmani, Shen Li, Philipp Gerlof, Trey Ideker, Danielle A Grotjahn, Elizabeth Villa, Le Song, and Pengtao Xie · 2024
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LAB-Bench: Measuring Capabilities of Language Models for Biology Research, July 2024
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, Michaela M. Hinks, Michael J. Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D. White, and Samuel G. Rodriques · 2024
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ProteinCLIP: enhancing protein language models with natural language
Kevin E Wu, Howard Chang, and James Zou · 2024
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Conditional language models enable the efficient design of proficient enzymes
Geraldene Munsamy, Ramiro Illanes-Vicioso, Silvia Funcillo, Sebastian Lindner, Gavin Ayres, Lesley S Sheehan, Steven Moss, Ulrich Eckhard, Philipp Lorenz, and Noelia Ferruz · 2024
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DeepES: Deep learning-based enzyme screening to identify orphan enzyme genes
Keisuke Hirota, Felix Salim, and Takuji Yamada · 2024
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Reactzyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, and Shuangjia Zheng · 2024
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EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights
Yong Liu, Chenqing Hua, Tao Zeng, Jiahua Rao, Zhongyue Zhang, Ruibo Wu, Connor W Coley, and Shuangjia Zheng · 2024
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Semantic mining of functional de novo genes from a genomic language model
Aditi T Merchant, Samuel H King, Eric Nguyen, and Brian L Hie · 2024
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ProtNote: a multimodal method for protein-function annotation
Samir Char, Nathaniel Corley, Sarah Alamdari, Kevin K Yang, and Ava P Amini · 2024
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Protein design meets biosecurity
David Baker and George Church · 2024
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Structure-based protein function prediction using graph convolutional networks
Vladimir Gligorijević, P. Douglas Renfrew, Tomasz Kosciolek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera Vlamakis, Ramnik J. Xavier, Rob Knight, Kyunghyun Cho, and Richard Bonneau · 2041
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Biocatalysed synthesis planning using data-driven learning
Daniel Probst, Matteo Manica, Yves Gaetan Nana Teukam, Alessandro Castrogiovanni, Federico Paratore, and Teodoro Laino · 2041
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EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions
Esther Heid, Daniel Probst, William H. Green, and Georg K. H. Madsen · 2041
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Merging enzymatic and synthetic chemistry with computational synthesis planning
Itai Levin, Mengjie Liu, Christopher A. Voigt, and Connor W. Coley · 2041
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Predicting enzymatic reactions with a molecular transformer
David Kreutter, Philippe Schwaller, and Jean-Louis Reymond · 2041
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Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures
Yidong Song, Qianmu Yuan, Sheng Chen, Yuansong Zeng, Huiying Zhao, and Yuedong Yang · 2041
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A general model to predict small molecule substrates of enzymes based on machine and deep learning
Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist, and Martin J. Lercher · 2041
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ProtGPT2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2041
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ProteInfer, deep neural networks for protein functional inference
Theo Sanderson, Maxwell L Bileschi, David Belanger, and Lucy J Colwell · 2050
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Curation of a list of chemicals in biosolids from EPA National Sewage Sludge Surveys & Biennial Review Reports
Tess Richman, Elyssa Arnold, and Antony J. Williams · 2052
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