@inproceedings{pei-etal-2023-biot5,
title = "{B}io{T}5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations",
author = "Pei, Qizhi and
Zhang, Wei and
Zhu, Jinhua and
Wu, Kehan and
Gao, Kaiyuan and
Wu, Lijun and
Xia, Yingce and
Yan, Rui",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.70",
doi = "10.18653/v1/2023.emnlp-main.70",
pages = "1102--1123",
abstract = "Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100{\%} robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://github.com/QizhiPei/BioT5.",
}
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<abstract>Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100% robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://github.com/QizhiPei/BioT5.</abstract>
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%0 Conference Proceedings
%T BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
%A Pei, Qizhi
%A Zhang, Wei
%A Zhu, Jinhua
%A Wu, Kehan
%A Gao, Kaiyuan
%A Wu, Lijun
%A Xia, Yingce
%A Yan, Rui
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F pei-etal-2023-biot5
%X Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose BioT5, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. BioT5 utilizes SELFIES for 100% robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, BioT5 distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at https://github.com/QizhiPei/BioT5.
%R 10.18653/v1/2023.emnlp-main.70
%U https://aclanthology.org/2023.emnlp-main.70
%U https://doi.org/10.18653/v1/2023.emnlp-main.70
%P 1102-1123
Markdown (Informal)
[BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://aclanthology.org/2023.emnlp-main.70) (Pei et al., EMNLP 2023)
ACL