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 LLMs 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 fine-tuning 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 at https://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications.

中文翻译：

---

SynAsk：在有机合成中释放大型语言模型的力量


随着大型语言模型 （LLMs性转变，彻底改变了各种语言任务和应用程序，LLMs 集成到专用域中增强了它们对特定领域应用程序的能力。值得注意的是，NLP 在有机化学方面取得了重大进展，特别是在预测合成任务方面，为开发为有机化学领域量身定制的 LLMs。在这项工作中，我们介绍了 SynAsk，这是一个由 AIChemEco Inc. 开发的综合有机化学领域特定LLM 平台。通过使用特定领域的数据微调 LLM 并将其与思维链方法集成，SynAsk 以问答的形式无缝访问我们的知识库和高级化学工具。这包括基础化学知识库、分子信息检索、反应性能预测、逆合成预测、化学文献获取等功能。这种新颖的方法将微调技术与外部资源整合协同作用，从而产生一种有机化学特异性模型，旨在促进该领域的研究和发现。SynAsk 可在 https://synask.aichemeco.com 访问，它代表了将 NLP 用于合成应用程序的重大进步。