SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910487006937088 |
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| author | Zhang, Chonghuan Lin, Qianghua Zhu, Biwei Yang, Haopeng Lian, Xiao Deng, Hao Zheng, Jiajun Liao, Kuangbiao |
| author_facet | Zhang, Chonghuan Lin, Qianghua Zhu, Biwei Yang, Haopeng Lian, Xiao Deng, Hao Zheng, Jiajun Liao, Kuangbiao |
| contents | The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLM into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By finetuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible via http://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04593 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis Zhang, Chonghuan Lin, Qianghua Zhu, Biwei Yang, Haopeng Lian, Xiao Deng, Hao Zheng, Jiajun Liao, Kuangbiao Chemical Physics Biomolecules The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLM into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By finetuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible via http://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications. |
| title | SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis |
| topic | Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2406.04593 |