ChatGPT can predict molecular properties and design new molecules

AI + Medicine Newsletter 2024–02–13

ChatGPT has already demonstrated mind-blowing capabilities in natural language understanding and processing. Its power can be greatly enhanced further to perform more complex tasks using prompt engineering, domain-specific fine-tuning, retrieval-augmented generation (RAG), and collaborative agents.

Can ChatGPT understand chemistry? A paper from Nature Machine Intelligence demonstrates that domain-specific fine-tuning of GPT-3 can perform classification and regression tasks for predicting molecular properties, such as solid-solution formation and Henry’s coefficient. It can even design new molecules based on instructions and specified properties. Traditionally, people have built complex ML or AI-based QSAR models for these tasks. If this new approach works, it will fundamentally change the approach in chemical and material sciences, and perhaps in all branches of science.

The fine-tuned model can perform comparably to, or even outperform, conventional machine learning techniques in a few datasets tested in the paper, particularly when the training dataset has a small sample size. It would be interesting to see how broadly this approach can be applied to various scenarios in predictive chemistry and how accurate.