@inproceedings{xiong-etal-2024-scimind,
title = "{S}ci{M}ind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences",
author = "Xiong, Zhaoping and
Fang, Xintao and
Chu, Haotian and
Wan, Xiaozhe and
Liu, Liwei and
Li, Yameng and
Xiang, Wenkai and
Zheng, Mingyue",
editor = "Edwards, Carl and
Wang, Qingyun and
Li, Manling and
Zhao, Lawrence and
Hope, Tom and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.langmol-1.8",
pages = "66--73",
abstract = "Large language models (LLMs) have made substantial strides, but their use in reliably tackling issues within specialized domains, particularly in interdisciplinary areas like pharmaceutical sciences, is hindered by data heterogeneity, knowledge complexity, unique objectives, and a spectrum of constraint conditions. In this area, diverse modalities such as nucleic acids, proteins, molecular structures, and natural language are often involved. We designed a specialized token set and introduced a new Mixture-of-Experts (MoEs) pre-training and fine-tuning strategy to unify these modalities in one model. With this strategy, we{'}ve created a multi-modal mixture-of-experts foundational model for pharmaceutical sciences, named SciMind. This model has undergone extensive pre-training on publicly accessible datasets including nucleic acid sequences, protein sequences, molecular structure strings, and biomedical texts, and delivers good performance on biomedical text comprehension, promoter prediction, protein function prediction, molecular description, and molecular generation.",
}
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%0 Conference Proceedings
%T SciMind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences
%A Xiong, Zhaoping
%A Fang, Xintao
%A Chu, Haotian
%A Wan, Xiaozhe
%A Liu, Liwei
%A Li, Yameng
%A Xiang, Wenkai
%A Zheng, Mingyue
%Y Edwards, Carl
%Y Wang, Qingyun
%Y Li, Manling
%Y Zhao, Lawrence
%Y Hope, Tom
%Y Ji, Heng
%S Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xiong-etal-2024-scimind
%X Large language models (LLMs) have made substantial strides, but their use in reliably tackling issues within specialized domains, particularly in interdisciplinary areas like pharmaceutical sciences, is hindered by data heterogeneity, knowledge complexity, unique objectives, and a spectrum of constraint conditions. In this area, diverse modalities such as nucleic acids, proteins, molecular structures, and natural language are often involved. We designed a specialized token set and introduced a new Mixture-of-Experts (MoEs) pre-training and fine-tuning strategy to unify these modalities in one model. With this strategy, we’ve created a multi-modal mixture-of-experts foundational model for pharmaceutical sciences, named SciMind. This model has undergone extensive pre-training on publicly accessible datasets including nucleic acid sequences, protein sequences, molecular structure strings, and biomedical texts, and delivers good performance on biomedical text comprehension, promoter prediction, protein function prediction, molecular description, and molecular generation.
%U https://aclanthology.org/2024.langmol-1.8
%P 66-73
Markdown (Informal)
[SciMind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences](https://aclanthology.org/2024.langmol-1.8) (Xiong et al., LangMol-WS 2024)
ACL
- Zhaoping Xiong, Xintao Fang, Haotian Chu, Xiaozhe Wan, Liwei Liu, Yameng Li, Wenkai Xiang, and Mingyue Zheng. 2024. SciMind: A Multimodal Mixture-of-Experts Model for Advancing Pharmaceutical Sciences. In Proceedings of the 1st Workshop on Language + Molecules (L+M 2024), pages 66–73, Bangkok, Thailand. Association for Computational Linguistics.