过量深度神经网络用于预测药物向相互作用
Xiao Xiaolin1,2,3, Liu Xiaozhi2,3, He Guoping4
1Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China.
iScience
|September 8, 2023
概括
过量DTI使用过量深度神经网络 (DNN) 来预测药物向相互作用 (DTI). 这种方法准确地模拟复杂的关系,通过识别潜在的候选药物来帮助药物发现.
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:
- 药物向相互作用 (DTI) 的预测对于有效的药物发现至关重要.
- 传统的实验方法用于DTI识别是昂贵和耗时的.
- 深度学习模型提供了一个有希望的替代方案,但面临着诸如过拟合等挑战.
研究的目的:
- 引入OverfitDTI,这是一个新的DTI预测框架,故意超越深度神经网络 (DNN).
- 通过过度装备的DNN来学习药物目标关系的隐式表示,以此作为杆.
- 证明OverfitDTI在准确预测DTI方面的有效性.
主要方法:
- 一个深度神经网络 (DNN) 模型故意过度适应学习来自药物化学空间和目标生物空间的特征.
- 过量DNN的学习权重作为非线性药物标关系的隐性表示.
- 在三个公开可用的数据集上评估了OverfitDTI框架.
主要成果:
- 过剩的DNN模型在适应非线性药物标关系方面表现出高准确性.
- 该框架成功地确定了15种预测与TEK (TIE-2) 相互作用的化合物.
- 实验验证证了AT9283和dorsomorphin作为HUVEC中的TEK抑制剂.
结论:
- 过度装配DNN可以成为捕捉DTI预测中复杂非线性关系的有效策略.
- OverfitDTI提供了一种计算效率高,准确的方法来加速药物发现.
- 该研究验证了计算方法在识别新型治疗剂方面的潜力.
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