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基于分子图像和分子描述器的预测模型用于药物查

Hideaki Mamada¹, Mari Takahashi¹, Mizuki Ogino¹

¹Drug Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, Osaka 569-1125, Japan.

|October 16, 2023

概括

这项研究开发了先进的计算模型,以预测药物毒性和药理动力学特性,减少动物试验. 结合深度学习和分子描述符,显著提高了药物发现查的预测准确性.

科学领域:

计算化学计算化学
药物发现药物发现药物发现
药理动力学药理动力学

背景情况:

药物发现需要广泛的毒性和药理动力学评估.
减少动物试验和开发成本需要高性能预测模型.
定量结构-活动关系 (QSAR) 分析对于开发这些模型至关重要.

研究的目的:

开发和比较50%致命剂量 (LD50),血脑屏障透 (BBBP) 和清除 (CL) 途径的预测模型.
评估深度学习 (DeepSnap-DL) 和基于分子描述器 (MD) 的方法的性能.
通过整体和共识建模来评估结合这些方法的好处.

主要方法:

使用DeepSnap-DL与复合图像作为功能构建预测模型.
使用分子操作环境,alvaDesc和ADMET预测器进行MD计算.
使用DataRobot开发基于MD的模型.
通过结合基于DeepSnap-DL和MD的预测,创建集体和共识模型.

主要成果:

在DeepSnap-DL模型中,AUC达到0.887 (LD50),0.893 (BBBP) 和0.883 (CL).
基于MD的模型实现了0.931 (LD50),0.919 (BBBP) 和0.900 (CL) 的AUC.
组合模型提高了性能 (AUC:0.942 LD50,0.936 BBBP,0.908 CL). 组合模型提高了性能 (AUC:0.942 LD50,0.936 BBBP,0.908 CL). 组合模型提高了性能 (AUC:0.942 LD50,0.936 BBBP,0.908 CL).
与整体方法相比,共识模型显示了优越的BAC (0.916LD50,0.918BBPP,0.847CL).

结论:

结合了DeepSnap-DL和MD的预测模型,对LD50,BBPP和CL路径预测具有很高的准确性.
集合和共识建模方法显著提高预测性能,而不是单一方法.
这种综合方法预计将通过提高查效率和降低实验成本来加速药物发现.

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