EnGCI:通过大型分子模型和KAN网络增强GPCR化合物相互作用预测
Weihao Liu1, Xiaoli Li1, Bo Hang1
1Computer School, Hubei University of Arts and Science, Longzhong Road, Xiangyang, 441053, Hubei, China.
BMC biology
|May 15, 2025
概括
新型深度学习模型EnGCI通过整合两个模块来增强GPCR化合物相互作用预测,这些模块从头开始学习分子特征,并利用预训练的大分子模型. 这种方法显著提高了药物发现应用的准确性.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
- G蛋白结合受体 (GPCR) 研究研究
背景情况:
- GPCR-化合物相互作用 (GCI) 对药物发现和化学遗传学至关重要.
- 深度学习模型,特别是大型分子模型,在提高GCI预测准确性方面表现有前途.
- 评估大型分子模型与其他深度学习架构的集成是一个关键的研究领域.
研究的目的:
- 调查大分子模型在GCI预测中的有效性.
- 开发和评估用于GCI预测的新型深度学习框架.
- 探索多式联运信息的融合,以提高GCI预测.
主要方法:
- 引入了由两个不同的模块组成的EnGCI模型:MSBM和LMMBM.
- MSBM使用图形同态网络 (GIN) 和卷积神经网络 (CNN) 与科尔莫戈罗夫-阿诺德网络 (KAN) 进行特征提取和决策.
- 对于特征提取,LMMBM采用了两种大型预训练模型,同时也使用KAN进行决策.
主要成果:
- 在精心策划的GCI数据集上,EnGCI模型实现了曲线下的面积 (AUC) 约为0.89.
- 拟议的模型显著优于现有的最先进的基准模型.
- 来自两个模块的多式联运信息的融合提高了整体GCI预测的准确性.
结论:
- EnGCI将从零开始的功能学习与预训练的大分子模型集成在一起,用于GCI预测.
- 该模型能够更深入地了解复杂的GPCR化合物相互作用关系.
- EnGCI提供了一个强大的框架,以推进GCI的预测能力,并帮助GPCR药物发现.
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