冷DTA:利用数据增强和基于注意力的特征融合来预测药物标结合亲和力
Kejie Fang1, Yiming Zhang2, Shiyu Du3
1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China.
Computers in biology and medicine
|August 19, 2023
概括
通过使用数据增强和注意力机制,ColdDTA增强了药物目标亲和力 (DTA) 对药物发现的预测. 这种方法在具有挑战性的冷启动场景中提高了模型概括性,优于现有的方法.
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.
- 深度学习模型在标准数据集上表现出色,但在新药或目标出现的现实世界冷启动问题上扎.
- 提高DTA预测模型的概括能力是一个重大挑战.
研究的目的:
- 开发一种新的深度学习框架,ColdDTA,以提高药物向亲和力预测的概括性能.
- 为了解决冷启动实验设置当前方法的局限性.
- 为DTA预测提供可解释的见解.
主要方法:
- 通过删除药物子图,ColdDTA通过生成新的药物标对来进行数据增强.
- 基于注意力的特征融合模块被用来更好地捕捉复杂的药物标相互作用.
- 该模型在三个基准数据集 (戴维斯,KIBA,BindingDB) 上使用冷启动实验进行了评估.
主要成果:
- 通过一致性指数 (CI) 和平均平方误差 (MSE) 测量,ColdDTA在戴维斯和KIBA数据集上的五种最先进的基线方法中表现优越.
- 在BindingDB数据集上,ColdDTA在分类任务中取得了更好的表现,以接收器运行特征曲线 (ROC-AUC) 下的面积表示.
- 模型重量可视化为预测过程提供了可解释的见解.
结论:
- 冷DTA有效地提高了对药物向 afinity 预测的概括能力,特别是在现实的冷启动场景中.
- 拟议的数据增强和基于注意力的融合策略增强了模型的稳定性和预测准确性.
- 公开可用的代码有助于进一步的药物发现研究和应用.
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