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Training Text-to-Molecule Models with Context-Aware Tokenization

Created by
  • Haebom

Author

Seojin Kim, Hyeontae Song, Jaehyun Nam, Jinwoo Shin

Outline

This paper proposes a novel text-to-molecule model, CAMT5, which introduces substructure-level tokenization to address the difficulty in capturing global structural information due to the atomic-level tokenization of existing text-to-molecule models. CAMT5 utilizes an importance-based learning strategy that prioritizes learning important substructures, focusing on the importance of substructures (e.g., ring systems). Experimental results show that CAMT5 outperforms existing state-of-the-art models, achieving superior performance even with only 2% of the training tokens. Furthermore, we propose an effective ensemble strategy that aggregates the outputs of text-to-molecule models.

Takeaways, Limitations

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Takeaways:
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We demonstrate that the performance of text-to-molecule models can be improved by tokenizing substructure units.
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It is suggested that learning efficiency can be improved through importance-based learning strategies.
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We demonstrate that additional performance improvements can be achieved through ensemble strategies.
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It suggests that excellent performance can be achieved even with small amounts of data.
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Limitations:
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Further research is needed to determine the generality of the proposed substructure tokenization method and its applicability to various molecular structures.
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Consideration should be given to the increased computational cost of ensemble strategies.
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Since this is a performance evaluation result for a specific dataset, it is necessary to verify generalization performance for other datasets.
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