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NG-DTA:使用n-gram分子图表进行药物向 afinity 预测

Lok-In Tsui, Te-Cheng Hsu, Che Lin

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

这项研究介绍了N-gram图形DTA (NG-DTA),这是一种用于药物标亲和力预测的深度学习模型. 通过将蛋白质表示为n克分子子图表,NG-DTA提高了准确性,优于现有的方法.

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:

药物向亲和力 (DTA) 预测对于加速药物开发至关重要.
当前的深度学习模型通常将蛋白质表示为1D字符串,与图表表示相比,限制了它们的信息容量.
图形神经网络 (GNN) 为分子数据分析提供了更强大的方法.

研究的目的:

为准确的DTA预测开发一种先进的深度学习方法.
调查蛋白质分子子图表表示对DTA预测性能的影响.
为药物发现管道建立一个新的计算工具.

主要方法:

开发了N-gram Graph DTA (NG-DTA),这是一个使用GNN的深度学习模型.
输入特征包括药物的分子图和蛋白质的n克分子子图.
没有使用蛋白质结构预测工具.

主要成果:

在戴维斯和基巴数据集上,NG-DTA实现了卓越的性能.
在戴维斯数据集上实现了0.905的一致性指数 (CI) 和0.196的平均平方误差 (MSE).
在Kiba数据集上达到0.904的CI和0.120的MSE.
证明n-gram蛋白质子图增强了用于DTA预测的深度学习模型性能.

结论:

使用n-gram分子子图表表示蛋白质显著改善了基于深度学习的DTA预测.
NG-DTA为计算药物发现提供了一个有希望和有效的方法.
该方法提供了准确的DTA预测,而不依赖蛋白质结构预测.

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