基于分子注意力转换器的环肽膜透性深度学习模型预测。

IF 3.9 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in bioinformatics Pub Date : 2025-03-11 eCollection Date: 2025-01-01 DOI:10.3389/fbinf.2025.1566174
Dawei Jiang, Zixi Chen, Hongli Du
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引用次数: 0

摘要

膜的通透性是环肽药物开发的关键瓶颈。实验膜透性测试是昂贵的,和精确的计算机预测工具是稀缺的。在这项研究中,我们建立了CPMP (https://github.com/panda1103/CPMP),一个基于分子注意力转换器(MAT)框架的环肽膜通透性预测模型。该模型显示出稳健的预测性能,PAMPA渗透率预测的决定系数(r2)为0.67,Caco-2、RRCK和MDCK细胞渗透率预测的r2值分别为0.75、0.62和0.73。它的性能优于传统的机器学习方法和基于图的神经网络模型。在消融实验中,我们验证了MAT架构中每个组件的有效性。此外,我们还分析了数据预训练和环肽构象优化对模型性能的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer.

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer.

Cyclic peptide membrane permeability prediction using deep learning model based on molecular attention transformer.

Membrane permeability is a critical bottleneck in the development of cyclic peptide drugs. Experimental membrane permeability testing is costly, and precise in silico prediction tools are scarce. In this study, we developed CPMP (https://github.com/panda1103/CPMP), a cyclic peptide membrane permeability prediction model based on the Molecular Attention Transformer (MAT) frame. The model demonstrated robust predictive performance, achieving determination coefficients (R 2 ) of 0.67 for PAMPA permeability prediction, and R 2 values of 0.75, 0.62, and 0.73 for Caco-2, RRCK, and MDCK cell permeability predictions, respectively. Its performance outperforms traditional machine learning methods and graph-based neural network models. In ablation experiments, we validated the effectiveness of each component in the MAT architecture. Additionally, we analyzed the impact of data pre-training and cyclic peptide conformation optimization on model performance.

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