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Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Elementary mathematical theory of classification and prediction
Taffee T Tanimoto. 1958 · 1958
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger. 1988 · 1988
Earlier work this paper cites.
Reoptimization of mdl keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse. 2002 · 2002
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014 · 2014
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017 · 2017
Earlier work this paper cites.
Prediction of human cytochrome p450 inhibition using a multitask deep autoencoder neural network
Xiang Li, Youjun Xu, Luhua Lai, and Jianfeng Pei. 2018 · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Earlier work this paper cites.
Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik. 2020 · 2020
Earlier work this paper cites.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
Earlier work this paper cites.
A deep learning approach to antibiotic discovery
Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M Donghia, Craig R MacNair, Shawn French, Lindsey A Carfrae, Zohar Bloom-Ackermann, et al. 2020 · 2020
Earlier work this paper cites.
Few-shot graph learning for molecular property prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, and Nitesh V Chawla. 2021 · 2021
Earlier work this paper cites.
Property-aware relation networks for few-shot molecular property prediction
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, and Dejing Dou. 2021 · 2021
Earlier work this paper cites.
Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Cited alongside, same era.
Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. 2022 · 2022
Cited alongside, same era.
Geomgcl: Geometric graph contrastive learning for molecular property prediction
Shuangli Li, Jingbo Zhou, Tong Xu, Dejing Dou, and Hui Xiong. 2022 · 2022
Cited alongside, same era.
Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
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Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan. 2023 · 2023
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Can large language models empower molecular property prediction?
Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, and Yong Liu. 2023 · 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 · 2023
Later among the works it cites.
S2gae: self-supervised graph autoencoders are generalizable learners with graph masking
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Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
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3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò. 2022 · 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 · 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 · 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 · 2022
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Unifying molecular and textual representations via multi-task language modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. 2023 · 2023
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Flashattention-2: Faster attention with better parallelism and work partitioning
Tri Dao. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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USearch by Unum Cloud
Ash Vardanian. 2023 · 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 · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J Ballard, Joshua Bambrick, et al. 2024 · 2024
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3d-molm: Towards 3d molecule-text interpretation in language models
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Git-mol: A multi-modal large language model for molecular science with graph, image, and text
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Fairness-guided few-shot prompting for large language models
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Introducing meta llama 3: The most capable openly available llm to date
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Discovery of a structural class of antibiotics with explainable deep learning
Felix Wong, Erica J Zheng, Jacqueline A Valeri, Nina M Donghia, Melis N Anahtar, Satotaka Omori, Alicia Li, Andres Cubillos-Ruiz, Aarti Krishnan, Wengong Jin, et al. 2024 · 2024
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Investigating the effectiveness of task-agnostic prefix prompt for instruction following
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Haiteng Zhao, Shengchao Liu, Ma Chang, Hannan Xu, Jie Fu, Zhihong Deng, Lingpeng Kong, and Qi Liu. 2024 · 2024
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Graph sampling-based meta-learning for molecular property prediction
Xiang Zhuang, Qiang Zhang, Bin Wu, Keyan Ding, Yin Fang, and Huajun Chen. 2023b · 2024
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