计算机科学
药品
下部结构
序列(生物学)
药物靶点
代表(政治)
人工智能
药物与药物的相互作用
图形
机器学习
计算生物学
数据挖掘
医学
药理学
理论计算机科学
化学
生物
工程类
法学
政治
结构工程
生物化学
政治学
作者
Junming Zhu,Chao Che,Hao Jiang,Xu Jin,Jiajun Yin,Zhong Zhang
标识
DOI:10.1186/s12859-024-05654-4
摘要
Abstract Background Drug–drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. Results In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. Conclusion The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.
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