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Predicting Drug–Protein Interactions through Branch-Chain Mining and multi-dimensional attention network

计算机科学 药物发现 水准点(测量) 人工智能 药物重新定位 卷积神经网络 机制(生物学) 机器学习 深度学习 重新调整用途 蛋白质结构预测 药物开发 数据挖掘 药品 生物信息学 蛋白质结构 化学 医学 生态学 哲学 生物化学 大地测量学 认识论 精神科 生物 地理
作者
Zhuo Huang,Qiu Xiao,Tuo Xiong,Wanwan Shi,Yide Yang,Guanghui Li
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:171: 108127-108127
标识
DOI:10.1016/j.compbiomed.2024.108127
摘要

Identifying drug–protein interactions (DPIs) is crucial in drug discovery and repurposing. Computational methods for precise DPI identification can expedite development timelines and reduce expenses compared with conventional experimental methods. Lately, deep learning techniques have been employed for predicting DPIs, enhancing these processes. Nevertheless, the limitations observed in prior studies, where many extract features from complete drug and protein entities, overlooking the crucial theoretical foundation that pharmacological responses are often correlated with specific substructures, can lead to poor predictive performance. Furthermore, certain substructure-focused research confines its exploration to a solitary fragment category, such as a functional group. In this study, addressing these constraints, we present an end-to-end framework termed BCMMDA for predicting DPIs. The framework considers various substructure types, including branch chains, common substructures, and specific fragments. We designed a specific feature learning module by combining our proposed multi-dimensional attention mechanism with convolutional neural networks (CNNs). Deep CNNs assist in capturing the synergistic effects among these fragment sets, enabling the extraction of relevant features of drugs and proteins. Meanwhile, the multi-dimensional attention mechanism refines the relationship between drug and protein features by assigning attention vectors to each drug compound and amino acid. This mechanism empowers the model to further concentrate on pivotal substructures and elements, thereby improving its ability to identify essential interactions in DPI prediction. We evaluated the performance of BCMMDA on four well-known benchmark datasets. The results indicated that BCMMDA outperformed state-of-the-art baseline models, demonstrating significant improvement in performance.
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