BiVAE-CPI:使用双边变异自编码器预测化合物-蛋白质相互作用的可解释生成模型
Yongxin Zhu1, Jianxin Wang1, Shiyue He1
1School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China.
Journal of chemical information and modeling
|August 21, 2025
概括
BiVAE-CPI是一种新的深度学习模型,通过考虑CPI对之间的相关性和学习共享潜伏表征来改善化合物-蛋白质相互作用 (CPI) 的预测. 这种方法提高了药物发现效率.
科学领域:
- 计算化学
- 生物信息学
- 药物发现
背景情况:
- 预测化合物-蛋白相互作用 (CPI) 对于药物发现至关重要,但与传统方法相比耗时.
- 深度学习模型越来越多地用于CPI预测,但许多模型未能有效地捕捉对对相关性或潜在表示.
研究的目的:
- 提出一个新的深度学习模型BiVAE-CPI,用于提高CPI预测.
- 通过结合CPI对之间的相关性和学习共享的低维隐藏表示来解决现有方法的局限性.
主要方法:
- 使用双边变量自编码器 (BiVAE) 来建模相关性和学习潜伏表示.
- 用于复合表示学习的使用图形同态网络 (GIN).
- 使用一个封闭的卷积编码器嵌入蛋白质序列.
主要成果:
- BiVAE-CPI在基准数据集上表现优于最先进的方法,特别是在不平衡的数据上.
- 该模型有效地捕获相关性,并学习共享的潜在表征以进行CPI预测.
- 连续隐藏空间表示提供了更好的解释性,并将分布与特征融合在一起.
结论:
- 在CPI预测准确性和效率方面,BiVAE提供了显著的进步.
- 考虑相关性和共享隐性表示对于开发可靠的CPI预测模型是有益的.
- 这种方法有助于加速药物发现和开发.
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