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.

翻译：未来开发能够整合多种实验数据并生成可检验假说的人工智能科学家工具潜力巨大。目前，定制化机器学习模型虽专精于单一科学任务，但缺乏通用模型的灵活性。本研究表明，通用大语言模型chatGPT 3.5-turbo可通过微调学习DNA结构生物物理特征。我们发现，通过微调模型使其返回链式思考响应，以及将多个微调后的子任务模型进行链式串联，均能显著提升DNA序列及其结构的分析与设计能力。