抗糖尿病肽（ADP）是具有潜在抗糖尿病活性的肽，在糖尿病的治疗和控制中具有重要意义。尽管具有治疗潜力，但由于数据有限、肽功能的复杂性以及传统湿实验室实验的昂贵且耗时的性质，ADP 的发现和预测仍然具有挑战性。本研究旨在通过探索使用先进深度学习技术发现和预测 ADP 的方法来应对这些挑战。具体来说，我们开发了两种模型：单通道 CNN 和三通道神经网络 (CNN RNN Bi-LSTM)。 ADP 主要从 BioDADPep 数据库收集，以及来自抗癌、抗菌和抗病毒肽数据集的数千个非 ADP。随后，使用进化尺度模型（ESM-2）进行数据预处理，然后通过10倍交叉验证进行模型训练和评估。此外，这项工作通过文献回顾收集了一系列新发表的 ADP 作为独立测试集，发现 CNN 模型在预测独立测试集上取得了最高的准确率（90.48%），超越了现有的 ADP 预测工具。最后考虑了模型的应用。使用 SeqGAN 生成新的候选 ADP，然后使用构建的 CNN 模型进行筛选。然后使用物理化学性质预测和结构预测来评估选定的肽的药物潜力。综上所述，本研究不仅建立了稳健的 ADP 预测模型，而且利用这些模型筛选了一批潜在的 ADP，解决了基于肽的抗糖尿病研究领域的关键需求。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.Copyright © 2024 Elsevier Ltd. All rights reserved.