基于多粒度不确定性感知的多模态表示学习的药物-靶点亲和力预测。

IF 5.4
Wenzhe Xu, Xiaorong Liu, Jie Wang, Fan Zhang, Dongfeng Hu, Dongfeng Hu
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引用次数: 0

摘要

动机:药物靶标亲和力的计算预测(DTA)在现代药物发现中起着至关重要的作用。然而,传统深度学习模型的有限可解释性以及化合物和蛋白质的多模态数据的异质性阻碍了它们在实际药物开发应用中的可靠性。结果:我们提出了一个新的不确定性感知多模态表示学习(UAMRL)框架来解决这些挑战。UAMRL采用双流编码器学习潜伏空间中药物与靶标之间的跨模态关联映射,并整合来自不同模态的异构信息。此外,引入了一种基于正态-逆-伽马分布的不确定性量化机制来模拟异构信息的可靠性,并抑制融合过程中不可信的贡献。实验表明,UAMRL在多个公共DTA数据集上取得了较好的预测精度,提高了预测性能和决策透明度。可用性:源代码可在https://github.com/Astraea2xu/UAMRL.Supplementary信息上获得;补充数据可在Bioinformatics在线上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAMRL: Multi-Granularity Uncertainty-Aware Multimodal Representation Learning for Drug-Target Affinity Prediction.

Motivation: Computational prediction of drug-target affinity (DTA) plays a critical role in modern drug discovery. However, the limited interpretability of traditional deep learning models and the heterogeneity of multimodal data from compounds and proteins hinder their reliability in practical drug development applications.

Results: We propose a novel Uncertainty-aware Multimodal Representation Learning (UAMRL) framework to address these challenges. UAMRL employs a dual-stream encoder to learn cross-modal association mappings between drugs and targets in a latent space and integrates heterogeneous information from different modalities. Moreover, an uncertainty quantification mechanism based on the Normal-Inverse-Gamma distribution is introduced to model the reliability of heterogeneous information and suppress less trustworthy contributions during fusion. Experiments show that UAMRL achieves superior predictive accuracy on multiple public DTA datasets, improving both prediction performance and decision transparency.

Availability: The source code is available at https://github.com/Astraea2xu/UAMRL.

Supplementary information: Supplementary data are available at Bioinformatics online.

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