DGDTA:动态图表注意力网络用于预测药物标结合亲和力
Haixia Zhai1, Hongli Hou1, Junwei Luo2
1School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.
BMC bioinformatics
|September 30, 2023
概括
本研究介绍了动态图DTA (DGDTA),这是一种用于预测药物标结合亲和力 (DTA) 的深度学习模型. 通过分析药物结构和蛋白质序列,DGDTA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.
- 机器学习在药物发现中的作用
背景情况:
- 准确的药物标结合亲和力 (DTA) 对于药物发现和重新定位至关重要.
- 现有的DTA预测方法需要进一步分析蛋白质和药物特征.
- 深度学习为改善DTA预测提供了一个有希望的途径.
研究的目的:
- 开发一种新的深度学习模型,用于准确的药物标结合亲和力预测.
- 加强对药物和蛋白质特征的分析,用于DTA预测.
- 改进计算机药物发现领域的现有方法.
主要方法:
- 拟议的动态图形DTA (DGDTA) 模型使用动态图形注意网络和双向长期短期记忆 (Bi-LSTM) 网络.
- 输入包括通过简化分子输入线输入系统 (SMILES) 和蛋白质氨基酸序列表示的药物化合物.
- 药物以图形形式建模,动态注意力得分突出重要原子和边缘;Bi-LSTM提取上下文蛋白质序列特征.
主要成果:
- 通过整合药物和蛋白质特征向量,DGDTA通过一个完全连接的层有效地预测DTA.
- 该模型利用药物特征提取的动态注意力和蛋白质序列分析的Bi-LSTM.
- 源代码在GitHub上公开提供.
结论:
- 与现有的方法相比,DGDTA在预测药物标结合亲和度方面表现出更高的准确性.
- 建议的深度学习方法提供了一种更有效的方法来预测DTA.
- 这一进步有助于更高效的药物发现和重新定位策略.
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