深度药物-目标结合亲和力预测基于多个特征提取和融合
Zepeng Li1, Yuni Zeng1, Mingfeng Jiang1
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China.
ACS omega
|January 27, 2025
概括
这项研究介绍了BTDHDTA,这是一种用于药物标结合亲和力 (DTA) 预测的新型深度学习模型. 通过捕捉药物和蛋白质数据中的复杂相关性,BTDHDTA提高了准确性,优于现有方法.
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:
- 准确的药物标结合亲和力 (DTA) 预测对于有效的药物发现至关重要.
- 当前的深度学习模型在表示药物/目标相关性和有效地融合相互作用数据方面面临挑战.
- 现有的方法经常使用简单的连接来进行交互学习,限制了对复杂关系的探索.
研究的目的:
- 开发一个先进的深度学习模型,用于增强DTA预测.
- 为了解决当前模型关于特征表示和相互作用的局限性.
- 提出BTDHDTA,一个基于端到端序列的模型,用于改进DTA预测.
主要方法:
- 使用双向封闭循环单元 (GRU),变压器编码器和扩展卷积来提取特征,捕获全球,本地和相关性模式.
- 实现了一个新的模块,将卷积神经网络 (CNN) 与高速公路连接相结合,用于深度特征融合.
- 开发了一个基于端到端序列的深度学习架构,命名为BTDHDTA.
主要成果:
- 与最先进的方法相比,BTDHDTA在基准数据集 (戴维斯,KIBA,梅茨) 上表现优越.
- 实现了改进的指标,包括平均平方误差 (MSE),一致性指数 (CI) 和R平方 (R2).
- 在一个案例研究中,成功地预测了FDA批准的药物与SARS-CoV-2蛋白质的结合 afinities,验证了实际效用.
结论:
- 在DTA预测准确性和效率方面,BTDHDTA提供了显著的进步.
- 该模型能够捕捉复杂的数据相关性和融合特征的能力提高了预测能力.
- BTDHDTA对现实世界的应用具有前景,包括药物重新定位和针对SARS-CoV-2等新型病原体的开发.
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