集成基于分子描述器和图形卷积网络的机器学习方法,用于预测MDR1和BCRP传送器的流量活动
Asahi Adachi1,2, Tomoki Yamashita1, Shigehiko Kanaya2
1Global DMPK, Takeda Pharmaceutical Company Limited, 26-1 Muraoka-Higashi, 2-Chome, Fujisawa, Kanagawa, 251-8555, Japan.
The AAPS journal
|September 12, 2023
概括
机器学习模型准确地预测了MDR1和BCRP的药物载体活性,有助于药物发现. 将分子描述符与图形卷积网络 (GCNs) 结合起来,改善了这些流量输送器的预测.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 多种药物耐药性 (MDR1) 和乳腺癌耐药性蛋白 (BCRP) 是影响药物吸收和分布的关键排泄输送物.
- 准确预测载体基质可以显著加速药物发现过程.
研究的目的:
- 开发和评估用于预测MDR1和BCRP流量活动的机器学习模型.
- 将基于描述符的机器学习方法与图形卷积网络 (GCNs) 的性能进行比较.
主要方法:
- 利用分子描述器和GCN来构建MDR1和BCRP流量活动的预测模型.
- 评估模型性能使用体外流量数据,内部数据集和具有时间划分的外部数据集.
- 对比了各种机器学习算法,包括CatBoost,支持向量回归,单任务,多任务和多式联网GCN,以及组合方法.
主要成果:
- 基于描述器的模型 (MDR1的CatBoost,BCRP的支持向量的回归) 显示出强大的预测性能 (R2).
- 组合模型结合了基于描述器的机器学习和GCN,在时间分割测试集中实现了最高的预测准确性 (MDR1的R2为0.706,BCRP的R2为0.587).
- 与单任务 GCN 相比,多式 GCN 改善了 BCRP 流量预测,表明了互补的结构信息.
结论:
- 先进的机器学习方法,特别是组合方法,对于预测MDR1和BCRP基质负担是有效的.
- 结合不同的分子表示策略 (描述器和GCN) 通过捕捉不同的分子特征来增强预测能力.
- 这些预测模型提供了一个有价值的工具,用于在药物发现管道的早期确定潜在的药物负债.
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