一种基于神经网络的新近邻方法,用于从化学结构中预测药物功能
Pranab Das1, Dilwar Hussain Mazumder2
1Department of Computer Science & Engineering, National Institute of Technology Nagaland, Kumar Bhaskar Varma Sanskrit and Ancient Studies University, Chumukedima, Dimapur, 797103, Nagaland, India.
European journal of pharmacology
|February 13, 2025
概括
这项研究引入了一种新的多标签卷积神经网络和近邻 (MLCNN-NN) 模型,用于从化学结构准确地预测药物功能,改善药物发现并减少失败.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 准确的药物功能预测对于药物发现,设计和开发至关重要.
- 误导药物功能有助于不良反应和发育失败.
- 计算方法可以在临床试验期间增强药物功能识别.
研究的目的:
- 提出一种基于神经网络的新近邻近方法,用于从化学二维结构中预测药物功能.
- 引入多标签卷积神经网络和近邻 (MLCNN-NN) 方法.
主要方法:
- 使用多标签卷积神经网络和近邻 (MLCNN-NN) 方法.
- 借助了这样的假设:类似的分子结构与类似的药物功能相关,同时发生的功能具有结构相似性.
- 在具有已知功能的药物分子的基准数据集上对模型进行了评估.
主要成果:
- 在药物功能的预测中,MLCNN-NN模型实现了98.10%的高精度.
- 超越了包括ResNet50,DenseNet201,MobileNetv2,Inceptionv3,VGG19和图形卷积网络 (GCN) 在内的已建立的模型.
- 在识别和可视化同时出现的药物功能方面表现出有效性,检测率为84.02%.
结论:
- 对于药物功能的预测,MLCNN-NN模型非常有效.
- 基于多标签神经网络的近邻方法显示了促进药物发现,设计和开发的巨大潜力.
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