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        <title>Training Text-to-Molecule Models with Context-Aware Tokenization</title>
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    <abstract>Recently, text-to-molecule models have shown great potential across various chemical applications, e.g., drug-discovery. These models adapt language models to molecular data by representing molecules as sequences of atoms. However, they rely on atom-level tokenizations, which primarily focus on modeling local connectivity, thereby limiting the ability of models to capture the global structural context within molecules. To tackle this issue, we propose a novel text-to-molecule model, coined Context-Aware Molecular T5 (CAMT5). Inspired by the significance of the substructure-level contexts in understanding molecule structures, e.g., ring systems, we introduce substructure-level tokenization for text-to-molecule models. Building on our tokenization scheme, we develop an importance-based training strategy that prioritizes key substructures, enabling CAMT5 to better capture the molecular semantics. Extensive experiments verify the superiority of CAMT5 in various text-to-molecule generation tasks. Intriguingly, we find that CAMT5 outperforms the state-of-the-art methods using only 2% of training tokens. In addition, we propose a simple yet effective ensemble strategy that aggregates the outputs of text-to-molecule models to further boost the generation performance.</abstract>
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%0 Conference Proceedings
%T Training Text-to-Molecule Models with Context-Aware Tokenization
%A Kim, Seojin
%A Song, Hyeontae
%A Nam, Jaehyun
%A Shin, Jinwoo
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kim-etal-2025-training
%X Recently, text-to-molecule models have shown great potential across various chemical applications, e.g., drug-discovery. These models adapt language models to molecular data by representing molecules as sequences of atoms. However, they rely on atom-level tokenizations, which primarily focus on modeling local connectivity, thereby limiting the ability of models to capture the global structural context within molecules. To tackle this issue, we propose a novel text-to-molecule model, coined Context-Aware Molecular T5 (CAMT5). Inspired by the significance of the substructure-level contexts in understanding molecule structures, e.g., ring systems, we introduce substructure-level tokenization for text-to-molecule models. Building on our tokenization scheme, we develop an importance-based training strategy that prioritizes key substructures, enabling CAMT5 to better capture the molecular semantics. Extensive experiments verify the superiority of CAMT5 in various text-to-molecule generation tasks. Intriguingly, we find that CAMT5 outperforms the state-of-the-art methods using only 2% of training tokens. In addition, we propose a simple yet effective ensemble strategy that aggregates the outputs of text-to-molecule models to further boost the generation performance.
%U https://aclanthology.org/2025.findings-emnlp.1221/
%P 22442-22460

Markdown (Informal)

[Training Text-to-Molecule Models with Context-Aware Tokenization](https://aclanthology.org/2025.findings-emnlp.1221/) (Kim et al., Findings 2025)

• Training Text-to-Molecule Models with Context-Aware Tokenization (Kim et al., Findings 2025)

ACL

• Seojin Kim, Hyeontae Song, Jaehyun Nam, and Jinwoo Shin. 2025. Training Text-to-Molecule Models with Context-Aware Tokenization. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22442–22460, Suzhou, China. Association for Computational Linguistics.