BatGPT-Chem: A Foundation Large Model for Chemical Engineering | JoVE Visualize

Area of Science:

Artificial Intelligence in Chemistry
Large Language Models (LLMs)
Chemical Engineering

Background:

Large language models (LLMs) show promise for AI in science, particularly chemistry, due to their ability to process sequential data.
Current applications of LLMs in chemistry are limited, with few models specifically designed for chemical data and tasks.
There is a need for advanced AI tools to model chemical sequences and natural language for diverse chemical applications.

Purpose of the Study:

To leverage LLMs for comprehensive modeling of chemical and natural language sequences to address various chemical engineering tasks.
To introduce BatGPT-Chem, a large-scale, bilingual foundation model tailored for chemical applications.
To enable full-spectrum prediction across chemical tasks by modeling information flow between chemical and natural language.

Main Methods:

Developed BatGPT-Chem, a 15-billion-parameter foundation model trained on over 100 million chemical instances.
Specialized BatGPT-Chem for 5 core tasks: retrosynthesis prediction, molecule design, molecule description, product inference, and yield prediction.
Enabled bilingual (English/Chinese) input/output and integrated explicit prediction of reaction conditions for retrosynthesis.

Main Results:

BatGPT-Chem demonstrated state-of-the-art performance in zero-shot evaluations, outperforming existing chemical LLMs and general models.
Achieved superior accuracy and validity across diverse tasks, particularly in predicting reactants and reaction conditions.
Showcased strong generalization capabilities, even in low-data settings, highlighting its practical utility.

Conclusions:

BatGPT-Chem represents a significant advancement in chemistry-specific LLMs, offering practical solutions for real-world applications.
The model's ability to predict reaction conditions explicitly addresses a critical gap in automated retrosynthesis.
BatGPT-Chem has strong potential to support and accelerate synthesis planning, drug discovery, and materials design.