基于图形特征和预训练的序列嵌入的多模式药物向亲和度预测.
Xin Tang1, Xiujuan Lei2, Lian Liu1
1School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
概括
我们开发了MGSDTA,这是一种新的多模式深度学习方法,用于预测药物标亲和力 (DTA). 通过整合图形和序列特征,MGSDTA比单模态方法提高了预测准确性.
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:
- 准确的药物向亲和力 (DTA) 预测对于有效的药物发现至关重要,从而降低实验成本.
- 现有的深度学习方法通常依赖于单个数据模式 (药物或目标特征).
研究的目的:
- 提出MGSDTA,一个多模式深度学习框架,用于增强DTA预测.
- 整合多样化的分子表示,以改进计算药物发现.
主要方法:
- 从药物和目标分子图表中提取特征.
- 使用先进的自我监督模型 (Mol2vec,ProtVec) 进行连续序列嵌入.
- 采用加权聚变模块来结合多模式特征用于DTA预测.
主要成果:
- MGSDTA的性能优于现有的单模式DTA预测方法.
- 图形和序列特征的集成显著提高了预测性能.
- 对基准数据集的验证证实了多模式方法的有效性.
结论:
- MGSDTA为DTA预测提供了一个更准确,更有效的计算方法.
- 多模式数据集成是推动药物发现的有希望的战略.
- 拟议的方法可以加快对潜在候选药物的选.
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