Fetching the paper…
Reading the bibliography…
Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs.
L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology , vol. 26, no. 3, pp. 297–302, 1945
1945
Earlier work this paper cites.
D. Weininger, “Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules,” Journal of chemical information and computer sciences , vol. 28, no. 1, pp. 31–36, 1988
1988
Earlier work this paper cites.
D. Butina, “Unsupervised data base clustering based on daylight’s fingerprint and tanimoto similarity: A fast and automated way to cluster small and large data sets,” Journal of Chemical Information and Computer Sciences , vol. 39, no. 4, pp. 747–750, 1999
1999
Earlier work this paper cites.
A. Aizawa, “An information-theoretic perspective of tf–idf measures,” Information Processing & Management , vol. 39, no. 1, pp. 45–65, 2003
2003
Earlier work this paper cites.
S. Robertson, H. Zaragoza et al. , “The probabilistic relevance framework: Bm25 and beyond,” Foundations and Trends® in Information Retrieval , vol. 3, no. 4, pp. 333–389, 2009
2009
Earlier work this paper cites.
Z. Wang, L. Liang, Z. Yin, and J. Lin, “Improving chemical similarity ensemble approach in target prediction,” Journal of cheminformatics , vol. 8, pp. 1–10, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” OpenAI , 2018
2018
Earlier work this paper cites.
B. Ding, Y. Weng, Y. Liu, C. Song, L. Yin, J. Yuan, Y. Ren, A. Lei, and C.-W. Chiang, “Selective photoredox trifluoromethylation of tryptophan-containing peptides,” European Journal of Organic Chemistry , vol. 2019, no. 46, pp. 7596–7605, 2019
2019
Earlier work this paper cites.
J. Arús-Pous, S. V. Johansson, O. Prykhodko, E. J. Bjerrum, C. Tyrchan, J.-L. Reymond, H. Chen, and O. Engkvist, “Randomized smiles strings improve the quality of molecular generative models,” Journal of cheminformatics , vol. 11, no. 1, pp. 1–13, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
S.-P. Peng and Y. Zhao, “Convolutional neural networks for the design and analysis of non-fullerene acceptors,” Journal of Chemical Information and Modeling , vol. 59, no. 12, pp. 4993–5001, 2019
2019
Earlier work this paper cites.
N. Q. K. Le, E. K. Y. Yapp, Y.-Y. Ou, and H.-Y. Yeh, “imotor-cnn: Identifying molecular functions of cytoskeleton motor proteins using 2d convolutional neural network via chou’s 5-step rule,” Analytical biochemistry , vol. 575, pp. 17–26, 2019
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
F. Grisoni, M. Moret, R. Lingwood, and G. Schneider, “Bidirectional molecule generation with recurrent neural networks,” Journal of chemical information and modeling , vol. 60, no. 3, pp. 1175–1183, 2020
2020
Earlier work this paper cites.
S. Amabilino, P. Pogány, S. D. Pickett, and D. V. Green, “Guidelines for recurrent neural network transfer learning-based molecular generation of focused libraries,” Journal of Chemical Information and Modeling , vol. 60, no. 12, pp. 5699–5713, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Cited alongside, same era.
Y. Weng, B. Ding, Y. Liu, C. Song, L.-Y. Chan, and C.-W. Chiang, “Late-stage photoredox c–h amidation of n-unprotected indole derivatives: Access to n-(indol-2-yl) amides,” Organic Letters , vol. 23, no. 7, pp. 2710–2714, 2021
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
N. Frey, R. Soklaski, S. Axelrod, S. Samsi, R. Gomez-Bombarelli, C. Coley, and V. Gadepally, “Neural scaling of deep chemical models,” chemrxiv , 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
C. Edwards, C. Zhai, and H. Ji, “Text2mol: Cross-modal molecule retrieval with natural language queries,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 595–607
2021
Cited alongside, same era.
V. Bagal, R. Aggarwal, P. Vinod, and U. D. Priyakumar, “Molgpt: molecular generation using a transformer-decoder model,” Journal of Chemical Information and Modeling , vol. 62, no. 9, pp. 2064–2076, 2021
2021
Cited alongside, same era.
J. Wang, C.-Y. Hsieh, M. Wang, X. Wang, Z. Wu, D. Jiang, B. Liao, X. Zhang, B. Yang, Q. He et al. , “Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning,” Nature Machine Intelligence , vol. 3, no. 10, pp. 914–922, 2021
2021
Cited alongside, same era.
F. Urbina and S. Ekins, “The commoditization of ai for molecule design,” Artificial Intelligence in the Life Sciences , vol. 2, p. 100031, 2022
2022
Cited alongside, same era.
C. Edwards, T. Lai, K. Ros, G. Honke, K. Cho, and H. Ji, “Translation between molecules and natural language,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 375–413. [Online]. Available: https://aclanthology.org/2022.emnlp-main.26
2022
Cited alongside, same era.
2022
Cited alongside, same era.
O. Rubin, J. Herzig, and J. Berant, “Learning to retrieve prompts for in-context learning,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 2655–2671
2022
Cited alongside, same era.
S. Min, M. Lewis, L. Zettlemoyer, and H. Hajishirzi, “Metaicl: Learning to learn in context,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 2791–2809
2022
Cited alongside, same era.
D. E. Coupry and P. Pogány, “Application of deep metric learning to molecular graph similarity,” Journal of Cheminformatics , vol. 14, no. 1, pp. 1–12, 2022
2022
Later among the works it cites.
J. Xu, Y. Li, J. Yang, S. Zhou, and W. Situ, “Plasma etching effect on the molecular structure of chitosan-based hydrogels and its biological properties,” International Journal of Biological Macromolecules , p. 123257, 2023
2023
Closest in time.
A. Higuchi, T.-C. Sung, T. Wang, Q.-D. Ling, S. S. Kumar, S.-T. Hsu, and A. Umezawa, “Material design for next-generation mrna vaccines using lipid nanoparticles,” Polymer Reviews , vol. 63, no. 2, pp. 394–436, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
W. Hu, Y. Liu, X. Chen, W. Chai, H. Chen, H. Wang, and G. Wang, “Deep learning methods for small molecule drug discovery: A survey,” IEEE Transactions on Artificial Intelligence , 2023
2023
Closest in time.
2023
Closest in time.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez et al. , “Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,” See https://vicuna. lmsys. org (accessed 14 April 2023) , 2023
2023
Closest in time.
2023
Closest in time.
A. D. White, “The future of chemistry is language,” Nature Reviews Chemistry , pp. 1–2, 2023
2023
Closest in time.