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Artificial intelligence has demonstrated immense potential in scientific research.
The swiss-prot protein sequence database and its supplement trembl in 2000
Amos Bairoch and Rolf Apweiler · 2000
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin et al · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Moleculenet: A benchmark for molecular machine learning, 2018
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2018
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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S2orc: The semantic scholar open research corpus
Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Dan S Weld · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Text2mol: Cross-modal molecule retrieval with natural language queries
Carl Edwards, ChengXiang Zhai, and Heng Ji · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao et al · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner et al · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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A survey of vision-language pre-trained models
Yifan Du, Zikang Liu, Junyi Li, and Wayne Xin Zhao · 2022
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Translation between molecules and natural language
C. Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 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
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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A molecular multimodal foundation model associating molecule graphs with natural language
Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen · 2022
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Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zheni Zeng, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2022
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Prot2text: Multimodal protein’s function generation with gnns and transformers
Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, and Michalis Vazirgiannis · 2023
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The impact of large language models on scientific discovery: a preliminary study using gpt-4
Microsoft Research AI4Science and Microsoft Azure Quantum · 2023
Cited alongside, same era.
Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
Biomedgpt: Open multimodal generative pre-trained transformer for biomedicine
Yizhen Luo, Jiahuan Zhang, Siqi Fan, Kai Yang, Yushuai Wu, Mu Qiao, and Zaiqing Nie · 2023
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Structured chemistry reasoning with large language models
Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Jiawei Han, and Lianhui Qin · 2023
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Biot5: Enriching cross-modal integration in biology with chemical knowledge and natural language associations
Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan · 2023
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Predictive chemistry augmented with text retrieval
Yujie Qian, Zhening Li, Zhengkai Tu, Connor Coley, and Regina Barzilay · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Augmenting large language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew White, and Philippe Schwaller · 2023
Cited alongside, same era.
He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, and Yu Li · 2023
Cited alongside, same era.
Unifying molecular and textual representations via multi-task language modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica · 2023
Cited alongside, same era.
Mol-instructions: A large-scale biomolecular instruction dataset for large language models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang, Kangwei Liu, Rui Huang, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2023
Cited alongside, same era.
What can large language models do in chemistry? a comprehensive benchmark on eight tasks
Taicheng Guo, Kehan Guo, et al · 2023
Cited alongside, same era.
Data-efficient molecular generation with hierarchical textual inversion
Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, and Jinwoo Shin · 2023
Cited alongside, same era.
Pubchem 2023 update
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, et al · 2023
Cited alongside, same era.
Andre Niyongabo Rubungo, Craig Arnold, Barry P Rand, and Adji Bousso Dieng · 2023
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Crossing new frontiers: Knowledge-augmented large language model prompting for zero-shot text-based de novo molecule design
Sagar Sakhinana and Venkataramana Runkana · 2023
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Enhancing activity prediction models in drug discovery with the ability to understand human language
Philipp Seidl, Andreu Vall, Sepp Hochreiter, and Günter Klambauer · 2023
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Relm: Leveraging language models for enhanced chemical reaction prediction
Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, and Xiang Wang · 2023
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Mollm: A unified language model to integrate biomedical text with 2d and 3d molecular representations
Xiangru Tang, Andrew Tran, Jeffrey Tan, and Mark B Gerstein · 2023
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Instructprotein: Aligning human and protein language via knowledge instruction
Zeyuan Wang, Qiang Zhang, Keyan Ding, Ming Qin, Xiang Zhuang, Xiaotong Li, and Huajun Chen · 2023
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Extracting human interpretable structure-property relationships in chemistry using XAI and large language models
Geemi Wellawatte and Philippe Schwaller · 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
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Generative pre-trained transformer: A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions, 2023
Gokul Yenduri, Ramalingam M, Chemmalar Selvi G, Supriya Y, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, Deepti Raj G, Rutvij H Jhaveri, Prabadevi B, Weizheng Wang, Athanasios V. Vasilakos, and Thippa Reddy Gadekallu · 2023
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Form follows function: Text-to-text conditional graph generation based on functional requirements
Peter A. Zachares et al · 2023
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MoleculeGPT: Instruction following large language models for molecular property prediction
Weitong Zhang, Xiaoyun Wang, Weili Nie, Joe Eaton, Brad Rees, and Quanquan Gu · 2023
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GIMLET: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Chang Ma, Hannan Xu, Jie Fu, Zhi-Hong Deng, Lingpeng Kong, and Qi Liu · 2023
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What a scientific language model knows and doesn’t know about chemistry
Lawrence Zhao, Carl Edwards, and Heng Ji · 2023
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou et al · 2023
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From artificially real to real: Leveraging pseudo data from large language models for low-resource molecule discovery
Yuhan Chen, Nuwa Xi, Yanrui Du, Haochun Wang, Chen Jianyu, Sendong Zhao, and Bing Qin · 2024
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Towards 3d molecule-text interpretation in language models
Sihang Li, Zhiyuan Liu, et al · 2024
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Towards unified ai drug discovery with multiple knowledge modalities
Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, and Zaiqing Nie · 2024
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Biobridge: Bridging biomedical foundation models via knowledge graph
Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, and RISHITA ANUBHAI · 2024
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