Zhou Mengyao, Xu Mengfan, Zhang Xiangling, Xing Xiaochun, Li Yang, Wang Guanghui, Yan Guiying
University of Chinese Academy of Sciences, Beijing, China.
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2025 Feb 6;20(2):e0318368. doi: 10.1371/journal.pone.0318368. eCollection 2025.
Over the years, synergistic drug combinations therapies have attracted widespread attention due to its advantages of overcoming drug resistance, increasing treatment efficacy and decreasing toxicity. Compared to lengthy medical drugs experimental screening, mathematical models and algorithms show great potential in synergistic drug combinations prediction. In this paper, we introduce a novel mathematical algorithm, the Human Pathway Relationship Network Algorithm (HPRNA), which is designed to predict synergistic drug combinations for angina pectoris. We first reconstruct a novel angina pectoris drug dataset, which include drug name, drug metabolism, chemical formula, targets and pathways, then construct a comprehensive human pathway network based on the genetic similarity of the pathways which contain information about the targets. Finally, we introduce a novel indicator to calculate drug pair scores which measure the likelihood of forming synergistic drug combination. Experimental results on angina pectoris drug datasets convincingly demonstrate that the HPRNA makes efficient use of target and pathway information and is superior to previous algorithms.
多年来，协同药物联合疗法因其克服耐药性、提高治疗效果和降低毒性等优点而受到广泛关注。与漫长的药物实验筛选相比，数学模型和算法在协同药物联合预测方面显示出巨大潜力。在本文中，我们介绍了一种新颖的数学算法，即人类通路关系网络算法（HPRNA），其旨在预测心绞痛的协同药物联合。我们首先重建了一个新颖的心绞痛药物数据集，该数据集包括药物名称、药物代谢、化学式、靶点和通路，然后基于包含靶点信息的通路的遗传相似性构建一个综合的人类通路网络。最后，我们引入了一种新颖的指标来计算药物对分数，该分数衡量形成协同药物联合的可能性。在心绞痛药物数据集上的实验结果令人信服地表明，HPRNA有效利用了靶点和通路信息，并且优于先前的算法。
