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深度混合CPI:用于化合物-蛋白相互作用预测的混合深度学习框架

Areen Rasool¹, Jamshaid Ul Rahman¹, Qasim Ali¹

¹Abdus Salam School of Mathematical Sciences GC University, Lahore, 54600, Pakistan.

Journal of molecular graphics & modelling

|January 21, 2026

概括

DeepHybridCPI是一种新的深度学习框架,通过将高级图形神经网络和卷积神经网络与长短期记忆相集成,增强化合物-蛋白相互作用 (CPI) 预测. 这种方法提高了药物发现效率和准确性.

科学领域:

生物信息学是一种生物信息学.
计算机化药物发现技术
药理学中的人工智能

背景情况:

预测化合物-蛋白相互作用 (CPI) 的深度学习方法对于虚拟查和药物发现至关重要.
使用浅图神经网络 (GNN) 和卷积神经网络 (CNN) 的现有方法在捕获全球化合物结构和长距离蛋白质依赖性方面存在局限性.
复杂的深度学习架构往往会产生大量的计算开销.

研究的目的:

引入DeepHybridCPI,这是一个混合深度学习框架,用于准确高效的CPI预测.
通过整合多尺度化合物特征提取和混合蛋白序列编码来克服现有方法的局限性.
为加速药物发现过程提供一个计算高效和有效的工具.

主要方法:

开发了一个混合深度学习框架,DeepHybridCPI.
它使用一个多尺度,密集连接的GNN来进行全面的复合特征提取 (局部子结构和全球拓).
与长短期记忆 (LSTM) 网络相结合的CNN用于蛋白质序列分析 (局部动机和远程依赖).
学习的化合物和蛋白质表示被融合在一个统一的潜在空间中,用于相互作用建模.

主要成果:

在基准人类和C. elegans数据集上,DeepHybridCPI表现出优于最先进的方法的性能.
该模型在曲线下的面积 (AUC),精度和回忆方面取得了显著的改进.
该框架有效地整合了各种分子和序列信息,以准确预测CPI.

结论:

在统一的框架中将多尺度复合表示与混合序列编码器相结合,对于CPI预测是有效的.
深度混合CPI为加速计算药物发现提供了一个有前途的方法.
开发的框架为预测化合物-蛋白质相互作用提供了高效和准确的解决方案.

关键词:

化合物-蛋白质相互作用卷积神经网络是一种卷积神经网络.药物发现药物发现长期短期记忆长期短期记忆多尺度图形神经网络的神经网络

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