有效的深度模型整体框架用于药物向相互作用预测
Jinhang Wei1, Yangbin Zhu1, Linlin Zhuo1
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
The journal of physical chemistry letters
|July 22, 2024
概括
这项研究介绍了EADTN,这是一种用于预测药物向相互作用 (DTI) 的整体模型,可以提高准确性并减少虚假阴性. 欧洲药物发现网络提高了药物发现的可靠性和可解释性,优于现有的方法.
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.
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测药物向相互作用 (DTI) 对于有效的药物开发至关重要.
- 现有的用于DTI预测的深度学习模型面临着性能和错误负面的挑战.
研究的目的:
- 提出EADTN,一种用于增强DTI预测的新型组合模型.
- 提高DTI预测模型的可靠性和可解释性.
- 通过实验性药物标相互作用识别来验证模型的有效性.
主要方法:
- 开发了EADTN,一个集成模型,具有创新的特征适应,用于局部重量提取.
- 实现了集群增强的参数微调,以减轻虚假负数.
- 利用Shapley基于价值的分析来识别关键的药物亚结构,并提高模型的解释性.
主要成果:
- 与最先进的模型相比,EADTN在各种数据集中表现出卓越的性能.
- 该模型成功地确定了NQO1目标和药物SIRT-IN-1和LY2183240.0之间的潜在相互作用.
- 湿实验室实验验证实了预测的药物向相互作用,证实了EADTN的可靠性.
结论:
- 在DTI预测方面,EADTN提供了显著的进步,提高了准确性并减少了虚假负面.
- 该模型的可解释性特征有助于理解药物机制和亚结构的重要性.
- 在药物重新定位和加速药物发现管道的应用中,EADTN显示出前景.
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