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Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refine molecular candidates.
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, and 1 others. 2020 · 1901
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.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger. 2010 · 2010
Earlier work this paper cites.
Rdkit documentation
Greg Landrum. 2013 · 2013
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin. 2015 · 2015
Earlier work this paper cites.
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Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. 2017 · 2017
Earlier work this paper cites.
A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Kelvin Luu, Xinyi Wu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A Smith. 2021 · 2021
Earlier work this paper cites.
Sample efficiency matters: a benchmark for practical molecular optimization
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Sure: Summarizing retrievals using answer candidates for open-domain qa of llms
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
Leveraging language model for advanced multiproperty molecular optimization via prompt engineering
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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