基于融合的深度学习架构,用于检测使用目标和药物序列和结构的药物标结合亲和力
IEEE journal of biomedical and health informatics
|September 13, 2023
概括
我们开发了CGraphDTA,这是一个新的计算模型,通过整合目标序列和结构来预测药物标结合亲和力. 这种方法提高了准确性,与现有方法相比,加速了药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 准确的药物标结合亲和力预测对于有效的药物发现至关重要.
- 现有的计算方法往往专注于目标序列或结构,忽视了集成信息.
- 湿实验室试验的结合亲和力是昂贵的和耗时的.
研究的目的:
- 开发一种新的计算模型,CGraphDTA,用于预测药物标结合亲和力.
- 整合目标序列和分子结构信息,以提高预测准确度.
- 通过增强的计算预测来加速药物发现过程.
主要方法:
- 开发了CGraphDTA,一种使用多尺度卷积神经网络 (CNN) 和图形神经网络 (GNN) 的融合协议.
- 利用CNN从药物和目标中提取基于序列的特征.
- 使用GNN从药物和向分子图中提取基于结构的特征.
主要成果:
- 在测试数据集上,CGraphDTA与最先进的方法相比,表现优越.
- 废弃性研究证实了综合序列和结构方法的有效性.
- 生物解释和药物选择性评估验证了CGraphDTA的实用性.
结论:
- 通过利用序列和结构数据,CGraphDTA有效地预测药物标结合亲和力.
- 该模型在现有方法上提供了显著的进步,加速了药物发现管道.
- CGraphDTA 作为一种有价值的计算工具,用于识别潜在的候选药物.
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