D-CyPre:一种基于机器学习的工具,用于准确预测人类CYP450酶代谢部位
Haolan Yang1,2, Jie Liu2, Kui Chen1,2
1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
PeerJ. Computer science
|June 10, 2024
概括
这项研究介绍了D-CyPre,这是一种使用图形神经网络和XGBOOST的新型工具,通过分析分子结构来预测细胞染色体P450酶上的代谢部位. 与现有方法相比,D-CyPre提供了更高的准确性和更全面的预测.
科学领域:
- 计算化学是一种计算化学.
- 药物代谢药物代谢
- 生物信息学是一种生物信息学.
背景情况:
- 图形神经网络 (GNN) 在预测代谢部位方面表现有前途.
- 现有的工具往往忽略了分子骨架信息.
- 将GNN与XGBOOST结合起来是一个强大的方法,尚未应用于代谢部位预测.
研究的目的:
- 开发和评估D-CyPre,这是一种用于预测代谢部位的新型计算工具.
- 整合原子,键和分子骨架信息,以提高预测准确度.
- 评估D-CyPre的性能与人类CYP450酶的BioTransformer和CyProduct等现有工具相比.
主要方法:
- 利用两个定向消息传递神经网络 (D-MPNN) 来处理分子信息.
- 集成的原子,键和分子骨架数据.
- 在最终预测模型中使用了XGBOOST.
- 在68个反应物的测试组上,在精度和回忆模式中评估了性能.
主要成果:
- 与BioTransformer和CyProduct相比,D-CyPre在人类CYP450代谢部位预测方面表现优越.
- 精确模式提供了高精度 (Jaccard: 0.497,F1: 0.660,精度: 0.737).
- 回忆模式提供了更全面的结果 (Jaccard: 0.506,F1: 0.669,回忆: 0.720).
- 当D-CyPre优于CyProduct时,观察到雅卡德和F1分数的显著改善.
结论:
- 通过整合全面的分子特征,D-CyPre有效地预测人类CYP450酶的代谢部位.
- 该工具提供灵活的预测模式 (精度和回忆),以满足不同的研究需求.
- D-CyPre在计算药物代谢预测方面取得了重大进展.
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