增强药物重新定位:用于药物向相互作用预测的多类组合模型,具有作用类型分类的行动类型分类
Leila Jafari Khouzani1, Soroush Sardari2, Soheila Jafari Khouzani3
1Department of Bioelectrics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
PloS one
|December 15, 2025
概括
本研究引入了一个多类分类框架,用于预测药物向相互作用 (DTI),将它们分类为激活剂,抑制剂或非作用. 基于直方图的渐变增强模型实现了87.90%的准确性,提高了药物重新定位的效率.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 计算机化药物发现技术
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于药物重新定位和降低制药开发成本至关重要.
- 现有的DTI预测方法通常将相互作用视为二进制,并忽视药理作用类型和非相互作用数据质量.
研究的目的:
- 为DTI预测开发一个多类分类框架,区分激活剂,抑制剂和无作用类.
- 提高数据集的多样性和可靠性,使用基于药物-药物和蛋白质-蛋白质相似性的新型零相互作用选择算法.
主要方法:
- 从DrugBank,PubChem和UniProt中提取了药物和蛋白质特征.
- 评估了各种特征选择和维度减少技术 (例如PCA,自动编码器,随机森林重要性).
- 对比特征集成策略 (连锁与卷积) 和评估分类器,包括集成方法,如基于直方图的梯度提升 (HGB) 和基于图形的模型,如HeteroGNN.
主要成果:
- 连接特征整合方法的表现优于卷积.
- 基于历史图的渐变增强 (HGB) 在外部测试组中实现了最高的整体预测准确度 (87.90%).
- 异种GNN提供了更平衡的阶级智能的表现,特别是在代表性不足的阶级.
结论:
- 拟议的多类分类框架为计算药物重新定位提供了一个可扩展和可解释的方法.
- 该框架通过提高DTI预测准确性和可靠性,支持更快,更具成本效益的潜在治疗候选者的识别.
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