  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.
