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The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions.
SciBERT: A Pretrained Language Model for Scientific Text
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Localization of the Tight Junction Protein Gene TJP1 to Human Chromosome 15q13, Distal to the Prader-Willi/Angelman Region, and to Mouse Chromosome 7
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A Diversity-Promoting Objective Function for Neural Conversation Models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Kevin Knight, Ani Nenkova, and Owen Rambow (Eds.). Association for Computational Linguistics, San Diego, California, 110–119
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DeepLoc: prediction of protein subcellular localization using deep learning
José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, and Ole Winther. 2017 · 2017
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Zinc-finger proteins in health and disease
Matteo Cassandri, Artem Smirnov, Flavia Novelli, Consuelo Pitolli, Massimiliano Agostini, Michal Malewicz, Gerry Melino, and Giuseppe Raschellà. 2017 · 2017
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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding. 2017 · 2017
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Unsupervised Feature Learning via Non-Parametric Instance Discrimination. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Zhirong Wu, Yuanjun Xiong, X Yu Stella, and Dahua Lin. 2018 · 2018
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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 · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola. 2019 · 2019
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Minimalist de novo design of protein catalysts
Liam R Marshall, Oleksii Zozulia, Zsofia Lengyel-Zhand, and Ivan V Korendovych. 2019 · 2019
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Evaluating protein transfer learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song. 2019 · 2019
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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. 2019 · 2019
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Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror. 2020 · 2020
Protein sequence and structure co-design with equivariant translation
Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, and Jian Tang. 2022 · 2022
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Generative de novo protein design with global context
Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, and Stan Z Li. 2022 · 2022
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High-resolution de novo structure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al · 2022
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Peer: a comprehensive and multi-task benchmark for protein sequence understanding
Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Ma Chang, Runcheng Liu, and Jian Tang. 2022 · 2022
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OntoProtein: Protein Pretraining With Gene Ontology Embedding
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Cited alongside, same era.
Is transfer learning necessary for protein landscape prediction?
Amir Shanehsazzadeh, David Belanger, and David Dohan. 2020 · 2020
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Wang Yu, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, and Burkhard Rost. 2021 · 2021
Cited alongside, same era.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021 · 2021
Cited alongside, same era.
Pfam: The protein families database in 2021
Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A Salazar, Erik LL Sonnhammer, Silvio CE Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, et al · 2021
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Clipcap: Clip prefix for image captioning
Ron Mokady, Amir Hertz, and Amit H Bermano. 2021 · 2021
Cited alongside, same era.
Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 139) , Marina Meila and Tong Zhang (Eds.). PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, and Huajun Chen. 2022 · 2022
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Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, and Kevin K Yang. 2023a · 2023
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Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex X. Lu, Nicolo Fusi, Ava P. Amini, and Kevin K. Yang. 2023b · 2023
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InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning. In Thirty-seventh Conference on Neural Information Processing Systems
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi. 2023 · 2023
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Illuminating protein space with a programmable generative model
John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent Frappier, Dana M Lord, Christopher Ng-Thow-Hing, Erik R Van Vlack, et al · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
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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, et al · 2023
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A text-guided protein design framework
Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar. 2023b · 2023
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Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2023
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Any-to-Any Generation via Composable Diffusion
Zineng Tang, Ziyi Yang, Chenguang Zhu, Michael Zeng, and Mohit Bansal. 2023 · 2023
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De novo design of protein structure and function with RFdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Minghao Xu, Xinyu Yuan, Santiago Miret, and Jian Tang. 2023 · 2023
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Kangfei Zhao, Yu Rong, Biaobin Jiang, Jianheng Tang, Hengtong Zhang, Jeffrey Xu Yu, and Peilin Zhao. 2023 · 2023
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Fast and accurate modeling and design of antibody-antigen complex using tFold
Fandi Wu, Yu Zhao, Jiaxiang Wu, Biaobin Jiang, Bing He, Longkai Huang, Chenchen Qin, Fan Yang, Ningqiao Huang, Yang Xiao, et al · 2024
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Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, and Yu Rong. 2024 · 2024
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