对细胞染色体P450 3A4,2D6和2C9抑制的机器学习模型的评估
Changda Gong1, Yanjun Feng1, Jieyu Zhu1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
传统的机器学习模型,特别是XGBoost和CatBoost,在预测细胞染色体P450 (CYP) 酶抑制方面优于深度学习. 这些发现有助于选择最佳的计算模型用于药物发现和预防药物相互作用.
科学领域:
- 药理学和化学信息学
- 计算机化药物发现技术
背景情况:
- 细胞染色体P450 (CYP) 酶会代谢约75%的药物.
- 抑制CYP可以导致不良的药物相互作用,需要预测模型.
研究的目的:
- 系统地评估用于预测CYP抑制的传统和深度学习模型.
- 确定用于P450抑制预测的最佳算法和分子表示.
主要方法:
- 评估了XGBoost,CatBoost和深度学习模型.
- 使用了指纹/物理化学描述符的组合特征.
- 在CYP3A4,CYP2D6和CYP2C9酶中评估性能.
主要成果:
- 结合功能的XGBoost和CatBoost实现了最高的曲线下面积 (AUC) 0.92.
- 深度学习模型显示平均AUC略低,为0.89.
- 数据量和采样策略对模型性能的影响最小.
结论:
- 传统的机器学习模型,特别是XGBoost和CatBoost,在CYP抑制预测方面优越.
- 结果指导了用于药物发现和安全评估的有效计算工具的选择.
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