邦迪:一种基于注意力机制的双向意图网络,用于基于注意力机制的药物目标相互作用识别
IEEE journal of biomedical and health informatics
|March 8, 2024
概括
新型深度学习框架BINDTI通过有效编码药物和蛋白质特征,准确预测药物向相互作用 (DTI). 这种方法的性能优于现有的方法,加速了药物发现和重新定位.
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:
- 鉴定药物向相互作用 (DTI) 对于药物发现至关重要,但体外方法昂贵且耗时.
- 深度学习对DTI预测有希望,但有效地表现药物和蛋白质特征仍然是一个挑战.
研究的目的:
- 开发一个端到端的深度学习框架,BINDTI,用于准确预测药物向相互作用.
- 为了应对在DTI预测中精确的药物和蛋白质特征表示的挑战.
主要方法:
- 宾迪利用图形卷积网络从二维分子图 (SMILES) 进行药物特征编码.
- 蛋白质特征是使用ACmix编码的,ACmix是一个混合模型,集成了对氨基酸序列的自我注意和卷积.
- 药物和目标特征通过双向意图网络融合,将意图和多头注意力结合起来,然后进行多层感知子分类.
主要成果:
- 在多个基准数据集 (BindingDB,BioSNAP,DrugBank,Human) 上,BINDTI显著超过了四种基线方法.
- 该框架在预测新型DTI方面表现出卓越的表现,特别是在不平衡的数据集上.
- 废弃性研究证实了双向意图网络和ACmix对预测准确性的重大贡献.
结论:
- BINDTI为DTI预测提供了一种有效和高效的方法,超越了现有的方法.
- 该框架有可能识别新的候选药物,增强虚拟查,并促进药物重新定位.
- 宾蒂推进了深度学习的应用,以加速药物发现管道.
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