Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
Harry L Morgan. 1965 · 1965
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.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn. 2010 · 2010
Earlier work this paper cites.
RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
Earlier work this paper cites.
Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Katrin Erk and Noah A. Smith (Eds.). Association for Computational Linguistics, Berlin, Germany, 1715–1725
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders
Esben Jannik Bjerrum and Boris Sattarov. 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.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Decoupled Weight Decay Regularization. In International Conference on Learning Representations
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Earlier work this paper cites.
Smiles-bert: large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics . 429–436
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. 2019 · 2019
Earlier work this paper cites.
Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
Molecular representations in AI-driven drug discovery: a review and practical guide
Laurianne David, Amol Thakkar, Rocío Mercado, and Ola Engkvist. 2020 · 2020
Earlier work this paper cites.
Measuring and Improving the Use of Graph Information in Graph Neural Networks. In International Conference on Learning Representations
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, and Ming-Chang Yang. 2020 · 2020
Earlier work this paper cites.
The message passing neural networks for chemical property prediction on SMILES
Jeonghee Jo, Bumju Kwak, Hyun-Soo Choi, and Sungroh Yoon. 2020 · 2020
Earlier work this paper cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Earlier work this paper cites.
Artificial intelligence in chemistry: current trends and future directions
Zachary J Baum, Xiang Yu, Philippe Y Ayala, Yanan Zhao, Steven P Watkins, and Qiongqiong Zhou. 2021 · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Chemberta-2: Towards chemical foundation models
Walid Ahmad, Elana Simon, Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar. 2022 · 2022
Cited alongside, same era.
Screening toward the Development of Fingerprints of Atomic Environments Using Bond-Orientational Order Parameters
Hideo Doi, Kazuaki Z Takahashi, and Takeshi Aoyagi. 2022 · 2022
Cited alongside, same era.
Translation between Molecules and Natural Language. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 375–413
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. 2022 · 2022
Cited alongside, same era.
Unifying molecular and textual representations via multi-task language modelling. In International Conference on Machine Learning . PMLR, 6140–6157
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. 2023 · 2023
Later among the works it cites.
What can large language models do in chemistry? a comprehensive benchmark on eight tasks
Taicheng Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh Chawla, Olaf Wiest, Xiangliang Zhang, et al · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Later among the works it cites.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning . PMLR, 19730–19742
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
Chemformer: a pre-trained transformer for computational chemistry
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum. 2022 · 2022
Cited alongside, same era.
Unified deep learning model for multitask reaction predictions with explanation
Jieyu Lu and Yingkai Zhang. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Multitask Prompted Training Enables Zero-Shot Task Generalization. In International Conference on Learning Representations
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Chemical-Reaction-Aware Molecule Representation Learning. In International Conference on Learning Representations
Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, and Martin D. Burke. 2022 · 2022
Cited alongside, same era.
The prediction of molecular toxicity based on BiGRU and GraphSAGE
Jianping Liu, Xiujuan Lei, Yuchen Zhang, and Yi Pan. 2023 · 2023
Later among the works it cites.
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , Houda Bouamor, Juan Pino, and Kalika Bali (Eds.). Association for Computational Linguistics, Singapore, 1102–1123
Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
When yield prediction does not yield prediction: an overview of the current challenges
Varvara Voinarovska, Mikhail Kabeshov, Dmytro Dudenko, Samuel Genheden, and Igor V Tetko. 2023 · 2023
Later among the works it cites.
Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules. In The Eleventh International Conference on Learning Representations
Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z. Li. 2023 · 2023
Later among the works it cites.
What a Scientific Language Model Knows and Doesn’t Know about Chemistry. In NeurIPS 2023 AI for Science Workshop
Lawrence Zhao, Carl Edwards, and Heng Ji. 2023 · 2023
Later among the works it cites.
Towards 3D Molecule-Text Interpretation in Language Models. In The Twelfth International Conference on Learning Representations
Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, and Qi Tian. 2024 · 2024
Closest in time.
BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning. In Findings of the Association for Computational Linguistics ACL 2024 , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand and virtual meeting, 1216–1240
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua Zhu, Shufang Xie, Tao Qin, and Rui Yan. 2024 · 2024
Closest in time.
Large language models for science and medicine
Amalio Telenti, Michael Auli, Brian L Hie, Cyrus Maher, Suchi Saria, and John PA Ioannidis. 2024 · 2024
Closest in time.
Understanding the limitations of deep models for molecular property prediction: Insights and solutions
Jun Xia, Lecheng Zhang, Xiao Zhu, Yue Liu, Zhangyang Gao, Bozhen Hu, Cheng Tan, Jiangbin Zheng, Siyuan Li, and Stan Z Li. 2024 · 2024
Closest in time.
Botao Yu, Frazier N Baker, Ziqi Chen, Xia Ning, and Huan Sun. 2024 · 2024
Closest in time.
Chemdfm: Dialogue foundation model for chemistry
Zihan Zhao, Da Ma, Lu Chen, Liangtai Sun, Zihao Li, Hongshen Xu, Zichen Zhu, Su Zhu, Shuai Fan, Guodong Shen, et al · 2024
Closest in time.