Woosang Lim
2024
Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction
Jun-Hyung Park
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Hyuntae Park
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Yeachan Kim
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Woosang Lim
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SangKeun Lee
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Pre-trained chemical language models (CLMs) excel in the field of molecular property prediction, utilizing string-based molecular descriptors such as SMILES for learning universal representations. However, such string-based descriptors implicitly contain limited structural information, which is closely associated with molecular property prediction. In this work, we introduce Moleco, a novel contrastive learning framework to enhance the understanding of molecular structures within CLMs. Based on the similarity of fingerprint vectors among different molecules, we train CLMs to distinguish structurally similar and dissimilar molecules in a contrastive manner. Experimental results demonstrate that Moleco significantly improves the molecular property prediction performance of CLMs, outperforming state-of-the-art models. Moreover, our in-depth analysis with diverse Moleco variants verifies that fingerprint vectors are highly effective features in improving CLMs’ understanding of the structural information of molecules.
2014
Optimizing Generative Dialog State Tracker via Cascading Gradient Descent
Byung-Jun Lee
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Woosang Lim
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Daejoong Kim
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Kee-Eung Kim
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)
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Co-authors
- Jun-Hyung Park 1
- Hyuntae Park 1
- Yeachan Kim 1
- SangKeun Lee 1
- Byung-Jun Lee 1
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