Chaining thoughts and LLMs to learn DNA structural biophysics
Tyler D. Ross Ashwin Gopinath
The future development of an AI scientist, a tool that is capable of
integrating a variety of experimental data and generating testable hypotheses,
holds immense potential. So far, bespoke machine learning models have been
created to specialize in singular scientific tasks, but otherwise lack the
flexibility of a general purpose model. Here, we show that a general purpose
large language model, chatGPT 3.5-turbo, can be fine-tuned to learn the
structural biophysics of DNA. We find that both fine-tuning models to return
chain-of-thought responses and chaining together models fine-tuned for subtasks
have an enhanced ability to analyze and design DNA sequences and their
structures.