DeepKinome:使用基于深度学习的回归模型的化合物对激酶结合亲和力的定量预测.
Yeeun Lee1, Jisu Eun2, Jinhyuk Lee2,3
1Department of Genome Medicine and Science, Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
Frontiers in molecular biosciences
|December 19, 2025
概括
深度学习模型DeepKinome准确地预测了激酶结合亲和力. 这一进步有助于理解激酶抑制,并通过分析复杂的化合物-蛋白相互作用来开发新药.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 酶对于细胞过程至关重要,是药物开发中的关键标.
- 由于复杂的数据,预测小分子和激酶之间的结合亲和力是复杂的.
研究的目的:
- 开发一个深度学习模型,DeepKinome,用于预测定量激酶化合物结合亲和力.
- 评估DeepKinome的性能与现有的机器学习和深度学习模型相比.
主要方法:
- 开发了一个基于20层卷积神经网络的深度学习回归模型 (DeepKinome).
- 该模型使用来自L1000数据库的234个激酶和163个化合物的数据进行训练.
- 使用根平均平方误差 (RMSE),R平方 (R2),皮尔森相关系数 (PCC) 和接受间隔比率 (AIR) 来评估性能.
主要成果:
- 与五个深度学习和四个机器学习模型相比,DeepKinome表现出更高的性能.
- 实现了1.157的RMSE,0.535的R2,0.743的PCC和0.570的AIR.
- 可解释的人工智能确定了影响预测的关键氨基酸序列,与已知的酶酸化部位相关.
结论:
- DeepKinome 提供了一种强大的方法来预测酶结合亲和力.
- 该模型增强了对激酶抑制机制的理解,并有助于开发新疗法.
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