Learning the Rules of Peptide Self-assembly through Data Mining with Large Language Models

Peptides are biologically ubiquitous and important molecules that self-assemble into diverse structures. While extensive research has explored the effects of chemical composition and environmental conditions on self-assembly, a systematic study consolidating this data to uncover global rules is lacking. In this work, we curate a peptide assembly database through a combination of manual processing by human experts and literature mining with a large language model. As a result, we collect more than 1,000 experimental data entries with information about peptide sequence, experimental conditions and corresponding self-assembly phases. Utilizing the data, machine learning models are trained and evaluated, demonstrating excellent accuracy (> 80%) and efficiency in assembly phase classification. Moreover, we fine-tune our GPT model for peptide literature mining with the developed dataset, which exhibits markedly superior performance in extracting information from academic publications relative to the pre-trained model. This workflow can improve efficiency when exploring potential self-assembling peptide candidates, through guiding experimental work, while also deepening our understanding of the mechanisms governing peptide self-assembly. --- phase_data_clean.csv stores 1000+ peptide self-assembly data under different experimental conditions. ---mined_paper_list.csv stores the corresponding papers we used to collect data. --- trainset.jsonl and testset.jsonl are data we used for fine-tuning the LLM. --- fine-tuning.ipynb: code used to fine-tune ChatGPT model. --- pretrain.ipynb: code used to test the pretrained ChatGPT model. --- train_and_inference.ipynb: code to use mined data to train and test a ML predictor for phase classification.肽是生物学中广泛存在且至关重要的分子，可自组装形成多样结构。尽管已有大量研究探讨了化学组成与环境条件对自组装过程的影响，但目前仍缺乏系统性整合此类数据以揭示全局规律的相关研究。本研究结合人类专家手动处理与大语言模型（Large Language Model）的文献挖掘手段，构建了肽组装数据库。最终我们收集到超过1000条实验数据条目，涵盖肽序列、实验条件及对应自组装相态的相关信息。 依托该数据集，我们训练并评估了机器学习模型，其在组装相态分类任务中展现出优异的准确率（>80%）与运行效率。此外，我们利用构建的数据集对GPT模型进行微调以用于肽相关文献挖掘；相较于预训练模型，该微调模型在学术文献信息提取任务中的表现显著更优。 该研究流程可通过指导实验工作提升潜在自组装肽候选物的探索效率，同时也能加深我们对肽自组装调控机制的理解。 --- phase_data_clean.csv 存储了1000余条不同实验条件下的肽自组装数据。 --- mined_paper_list.csv 存储了我们用于收集数据的对应文献。 --- trainset.jsonl 与 testset.jsonl 为我们用于微调大语言模型的数据集。 --- fine-tuning.ipynb：用于微调ChatGPT模型的代码。 --- pretrain.ipynb：用于测试预训练ChatGPT模型的代码。 --- train_and_inference.ipynb：用于利用挖掘得到的数据训练并测试相态分类机器学习预测器的代码。