Chaining thoughts and LLMs to learn DNA structural biophysics

arXiv:2403.01332v1 Announce Type: cross
Abstract: 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 …

abstract ai scientist arxiv cs.ai cs.lg data development dna experimental flexibility future learn llms machine machine learning machine learning models q-bio.qm singular tasks thoughts tool type