DO-GMA:一个端到端的药物向相互作用识别框架,具有深度超参数化的卷积网络和门式多头注意力机制
Lihong Peng1, Jiale Mao1, Guohua Huang2
1School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
Journal of chemical information and modeling
|January 28, 2025
概括
这项研究介绍了DO-GMA,这是一个新的深度学习框架,用于识别潜在的药物向相互作用 (DTI). DO-GMA通过整合多种药物和蛋白质特征来显著提高DTI预测的准确性,以加强药物发现.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 在药理学中的机器学习.
背景情况:
- 准确识别药物向相互作用 (DTI) 对药物发现和重新使用至关重要.
- 现有的深度学习方法通常使用单一视角的药物特征,需要改进的特征融合技术.
- 需要先进的计算框架来提高DTI预测的准确性.
研究的目的:
- 开发一个新的端到端深度学习框架,DO-GMA,用于识别潜在的药物向相互作用 (DTI).
- 通过结合多视角药物特征和先进特征融合来解决当前方法的局限性.
- 提高用于药物发现的DTI预测的准确性和可靠性.
主要方法:
- 开发了DO-GMA,集成了一个深度超参数化的卷积神经网络 (DO-CNN) 和一个门式多头注意力 (GMA) 机制.
- 从SMILES字符串和2D分子图 (通过GCN) 学习了药物表示,并从氨基酸序列中学习了蛋白质表示.
- 融合了药物和蛋白质特征,使用封闭注意力,使用共享学习查询的多头注意力和双线连接,然后使用多层感知器进行分类.
主要成果:
- 在四个数据集和多个指标 (AUC,AUPR,精度,F1分数,MCC) 中,DO-GMA显著超过了六种最先进的DTI预测方法.
- 废除研究,统计分析,参数灵敏度分析和特征可视化证实了DO-GMA的强大性能.
- 该框架成功预测了两个特定的药物-蛋白质对的潜在相互作用.
结论:
- DO-GMA提供了一个强大而有效的框架,用于识别潜在的药物向相互作用.
- 拟议的多视角特征表示和融合策略提高了DTI预测的准确性.
- DO-GMA提供了一种有价值的工具,可以加速药物发现和重新定位努力.
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