E Uma, T Mala
Department of Information Science and Technology, College of Engineering Guindy, Chennai, India.
Department of Information Science and Technology, College of Engineering Guindy, Chennai, India.
Comput Biol Chem. 2025 Oct;118:108472. doi: 10.1016/j.compbiolchem.2025.108472. Epub 2025 Apr 23.
Drug-Target Affinity (DTA) prediction plays a crucial role in the drug discovery process by evaluating the strength of the interaction between a drug and its biological target, which is often a protein. Despite advancements in DTA prediction through deep learning, several fundamental challenges persist: (i) suboptimal information propagation in molecular graphs, limiting the effective representation of complex drug structures, (ii) accurately modeling the complex interactions between drug-binding sites and protein substructures, and (iii) prioritizing critical substructure interactions to enhance both accuracy and interpretability. To address these challenges, the DICCA-DTA framework is introduced, aiming to improve the contextual integration of molecular information and facilitate a more comprehensive representation of drug-target interactions in allopathic research. It employs a Diffused Isomorphic Network (DIN) to extract comprehensive drug features from molecular graphs, capturing both local substructures and global information. Furthermore, a Contextualized Capsule Attention Network (CCAN) module incorporates multi-head attention with capsule networks to capture both local and global protein sequence characteristics. The attention-guided Factorized Cross-Pooling (FCP) mechanism dynamically refines drug-protein interaction modeling by selectively emphasizing critical binding site interactions, thereby enhancing predictive accuracy. Explainable attention maps further reveal the most crucial drug-protein binding site interactions, providing transparent insights into the model's decision-making process. Comprehensive evaluations across the Davis, KIBA, Metz and BindingDB datasets demonstrate the superior performance of the DICCA-DTA framework over existing state-of-the-art models. A case study on cancer-related protein interactions from the DrugBank database further demonstrates the framework's precision in identifying key drug-protein affinities, reinforcing its potential to accelerate drug discovery and repurposing.
药物-靶点亲和力（DTA）预测在药物发现过程中起着至关重要的作用，它通过评估药物与其生物靶点（通常是一种蛋白质）之间相互作用的强度来实现。尽管通过深度学习在DTA预测方面取得了进展，但仍存在一些基本挑战：（i）分子图中的信息传播不理想，限制了复杂药物结构的有效表示；（ii）准确建模药物结合位点与蛋白质亚结构之间的复杂相互作用；（iii）对关键亚结构相互作用进行优先级排序，以提高准确性和可解释性。为了解决这些挑战，引入了DICCA-DTA框架，旨在改善分子信息的上下文整合，并促进在对抗疗法研究中对药物-靶点相互作用进行更全面的表示。它采用扩散同构网络（DIN）从分子图中提取全面的药物特征，捕获局部亚结构和全局信息。此外，上下文胶囊注意力网络（CCAN）模块将多头注意力与胶囊网络相结合，以捕获局部和全局蛋白质序列特征。注意力引导的因子分解交叉池化（FCP）机制通过有选择地强调关键结合位点相互作用来动态优化药物-蛋白质相互作用建模，从而提高预测准确性。可解释的注意力图进一步揭示了最关键的药物-蛋白质结合位点相互作用，为模型的决策过程提供了透明的见解。在Davis、KIBA、Metz和BindingDB数据集上的综合评估表明，DICCA-DTA框架比现有的最先进模型具有更优的性能。来自DrugBank数据库的癌症相关蛋白质相互作用的案例研究进一步证明了该框架在识别关键药物-蛋白质亲和力方面的准确性，增强了其加速药物发现和药物重新利用的潜力。
