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ChatMol: interactive molecular discovery with natural language.

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We introduce conversational molecular design, using natural language to describe and edit molecules. Our ChatMol model, enhanced with chemical knowledge, demonstrates effectiveness in this challenging task.

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Natural language processing (NLP) is increasingly vital for human-machine interaction.
  • Biochemical tasks like property prediction and molecule mining are complex.
  • Integrating natural and chemical languages enhances molecular understanding and task efficiency.

Purpose of the Study:

  • Introduce conversational molecular design for describing and editing molecules via natural language.
  • Develop ChatMol, a generative pretrained model for this novel task.
  • Evaluate ChatMol's effectiveness against existing large language models.

Main Methods:

  • Developed ChatMol, a generative pretrained model.
  • Enhanced ChatMol with experimental property data and molecular spatial information.
  • Incorporated associations between natural and chemical languages.
  • Evaluated model performance against benchmarks like ChatGPT.

Main Results:

  • Conversational molecular design presents significant challenges.
  • ChatMol demonstrates effectiveness, outperforming typical large language models.
  • Knowledge enhancement is crucial for successful conversational molecular design.

Conclusions:

  • Conversational molecular design is a promising new area for molecular discovery.
  • ChatMol provides a strong foundation for natural language-driven molecular manipulation.
  • Further research can explore advanced natural language interactions in chemistry.