SMVSNN:使用尖端多视图罗神经网络进行抗癌药物相互作用预测的智能框架
Guoliang Tan1, Yijun Liu1, Wujian Ye1
1School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China.
Journal of chemical information and modeling
|May 21, 2025
概括
这项研究引入了一种新的Spiking多视图罗神经网络 (SMVSNN) 框架,用于预测癌症治疗中的协同药物组合. SMVSNN增强了药物相互作用分析,在识别有效的抗癌治疗方面表现优于以前的模型.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 协同作用的药物组合对于有效的癌症治疗至关重要,提高疗效和减少副作用.
- 药物相互作用 (DDI) 分析支持这一点,智能算法越来越多地用于预测.
- 现有的计算方法在功能处理和数据集成方面存在局限性.
研究的目的:
- 为分析和预测药物相互作用 (DDI) 提出一个新的尖端多视图罗神经网络 (SMVSNN) 框架.
- 解决DDI预测中以前计算方法的局限性.
- 加强用于癌症治疗的协同药物组合的识别.
主要方法:
- 利用一个凸卷积网络和凸神经感知器来处理多个视图中的药物对特征.
- 将集成的多视图功能整合到一个统一的表示中,使用自学注意力权重模块.
- 采用尖端多层感知器网络用于最终的DDI预测.
主要成果:
- 与传统的智能算法相比,SMVSNN框架显示出更高的性能.
- 实际的抗癌药物数据 (904种药物,7730个DDI记录) 用于验证.
- 五倍交叉验证显示SMVSNN在大多数指标上表现优于以前的模型.
结论:
- SMVSNN框架是推断抗癌治疗中潜在的协同作用药物组合的有效方法.
- 在SMVSNN中,尖端神经元和罗网络擅长从药物对数据中提取和整合潜在信息.
- 这种方法为推进癌症治疗策略提供了一个有前途的计算工具.
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