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基于自我注意的层次式多式联络图形神经网络用于DTI预测
Jilong Bian1, Hao Lu1, Guanghui Dong1
1College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040, China.
预测药物向相互作用 (DTI) 对药物开发至关重要. 一个新的分层多式联络自我注意网络 (HMSA-DTI) 通过融合多种药物和蛋白质数据,捕捉复杂的相互作用,提高了预测准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 药物向相互作用 (DTI) 对于有效的药物开发至关重要.
- 当前的深度学习模型通常依赖于单个数据表示,限制了全面的特征分析.
- 现有的多式联运方法无法同时捕捉内部和跨式联运相互作用,阻碍了DTI预测的准确性.
研究的目的:
- 提出一种新的深度学习模型,用于准确高效地预测药物向相互作用.
- 解决现有的DTI预测模型中单模表示和不完整的多模融合的局限性.
- 通过同时考虑模式内和模式间的相互作用来增强特征表示能力.
主要方法:
- 开发了一个基于自我注意的层次式多式模式图形神经网络 (HMSA-DTI).
- 输入数据包括药物SMILES,分子图,蛋白质序列和2-mer序列.
- 采用了层次的多模式自我注意机制,用于药物和蛋白质特征的深度融合,捕捉了模式内和模式间的相互作用.
主要成果:
- 拟议的HMSA-DTI模型与基线方法相比显示出更高的性能.
- 在多个评估指标上取得了显著的优势.
- 在五个基准数据集上得到验证,证实了其在DTI预测中的有效性.
结论:
- HMSA-DTI有效地整合了多式联运数据,用于增强DTI预测.
- 层次的多式联运自注意机制捕捉到关键的内部和跨式联运互动.
- 这种方法通过准确的DTI预测,为提高药物开发效率提供了一个有希望的方向.
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