Du Zhenjiao, Kumar Nandan, Li Yonghui
Department of Grain Science and Industry, Kansas State University, Manhattan, KS, USA.
Methods Mol Biol. 2025;2941:279-292. doi: 10.1007/978-1-0716-4623-6_17.
The unified prediction model architecture, UniDL4BioPep, offers a significant advancement in applying machine learning approaches to bioactive peptide discovery. By streamlining the model development process, this architecture reduces the effort required to create custom models, allowing wet-lab researchers to accelerate scientific discovery by easily tailoring models to their specific needs. UniDL4BioPep leverages protein language models, specifically evolutionary scale modeling (ESM), and simplifies model preparation to a single click, making it both accessible and efficient for users. This chapter provides the technical details and practical operation guide for utilizing this unified architecture and demonstrates its effectiveness in binary classification tasks.
统一预测模型架构UniDL4BioPep在将机器学习方法应用于生物活性肽发现方面取得了重大进展。通过简化模型开发过程，该架构减少了创建定制模型所需的工作量，使湿实验室研究人员能够通过轻松根据其特定需求定制模型来加速科学发现。UniDL4BioPep利用蛋白质语言模型，特别是进化尺度建模（ESM），并将模型准备简化为一键操作，使其对用户来说既易于使用又高效。本章提供了使用此统一架构的技术细节和实际操作指南，并展示了其在二元分类任务中的有效性。
