多式联络CNN-DDI:使用多式联络CNN来检测与药物相互作用相关的事件
Muhammad Asfand-E-Yar1, Qadeer Hashir1, Asghar Ali Shah2
1Department of Computer Science, CoE-AI, Center of Excellence Artificial Intelligence, Bahria University, Islamabad, Pakistan.
Scientific reports
|February 20, 2024
概括
本研究引入了一种多模式卷积神经网络 (MCNN-DDI) 模型,用于预测药物对药物相互作用 (DDI). 该模型通过整合化学结构,点和酶来实现90%的准确性,以提高DDI事件预测.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 机器学习在药物发现中的作用
背景情况:
- 药物相互作用 (DDI) 发生在患者服用多种药物时,可能会影响药物的疗效.
- 检测DDI对于患者的安全至关重要,涉及分析化学子结构,标,途径和酶.
- 机器学习 (ML) 和深度学习 (DL) 为识别复杂的DDI事件提供了强大的工具.
研究的目的:
- 开发和评估一种新的深度学习模型,用于预测药物对药物相互作用 (DDI).
- 评估在DDI预测中整合多种药物特征 (包括化学结构,标和酶) 的有效性.
- 将拟议模型的性能与传统预测算法进行比较.
主要方法:
- 使用多模式卷积神经网络 (MCNN-DDI) 模型进行DDI预测.
- 输入特征包括药物化学结构 (SMILES),酶,途径和药物点.
- 该模型包含多个层,激活功能和药物特性,以优化预测准确性.
主要成果:
- 该MCNN-DDI模型实现了90.00%的高精度和94.78%的精度回忆曲线下的面积 (AUPR).
- 实验表明,结合药物特征 (化学子结构,标,酶) 显著改善了DDI事件预测.
- 该模型在准确性方面超过了传统的预测算法.
结论:
- 通过利用多模式药物特征,MCNN-DDI模型有效地预测药物相互作用.
- 整合各种药物信息,如化学基结构,标和酶,对于DDI预测来说是优越的.
- 这种深度学习方法为提高药物安全性和疗效监测提供了一个有希望的工具.
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