一个优化的移动网络V2注意力并行网络,通过结合本地和全球特征来预测药物相互作用
S K Mydhili1, S Nithyaselvakumari2, K Padmanaban3
1Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, India.
Biopharmaceutics & drug disposition
|March 12, 2025
概括
这项研究引入了MV2SAPCNNO,这是一种用于预测药物相互作用 (DDI) 的新方法. 该模型提高了准确性和效率,有助于更安全的药物管理和药物开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 药物相互作用 (DDI) 对患者安全构成重大风险,并使药物开发复杂化.
- 准确的DDI预测对于有效的药物管理和降低风险至关重要.
研究的目的:
- 开发和评估一种新的技术,MV2SAPCNNO,以提高DDI预测的精度.
- 提高DDI预测模型的准确性和效率.
主要方法:
- 数据预处理,包括规范化和降噪.
- 使用MobileNetV2与简化注意网络 (MV2SAN) 进行特征提取,用于本地和全球特征.
- 平行卷积神经网络 (PCNN) 处理被纳尔瓦尔优化器 (NO) 优化,用于参数调整和最小化错误.
主要成果:
- 与现有的DDI预测模型相比,MV2SAPCNNO模型表现出卓越的性能.
- 实现了增强的准确性,精度,回忆和F-score指标.
- 纳尔瓦尔优化器提高了融合效率,减少了计算时间.
结论:
- MV2SAPCNNO提供了一种高效准确的DDI预测方法.
- 该模型的性能有助于更安全的药物管理和药物开发.
- 这些发现支持在临床实践中加强患者安全.
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