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The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
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Ross Irwin, Spyridon Dimitriadis, Jiazhen He, and Esben Jannik Bjerrum · 2022
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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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Multi-modal molecule structure–text model for text-based retrieval and editing
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Molca: Molecular graph-language modeling with cross-modal projector and uni-modal adapter
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Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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Llama: Open and efficient foundation language models (2023)
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Visual instruction tuning
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Git-mol: A multi-modal large language model for molecular science with graph, image, and text
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Learning invariant molecular representation in latent discrete space
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