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使用传统的机器学习和先进的深度学习技术进行hERG-毒性预测

Erik Ylipää¹, Swapnil Chavan², Maria Bånkestad¹

¹Computer Systems Unit, Research Institutes of Sweden RISE, Kista 164 40, Sweden.

Current research in toxicology

|September 13, 2023

概括

人工智能 (AI) 模型,包括图形神经网络 (GNN),准确地预测人类以太-a-go-go相关基因 (hERG) 毒性. GNN为制药开发和毒理学查提供了强大的,自动化的方法.

科学领域:

计算化学和毒理学计算化学和毒理学
人工智能在药物发现中的作用

背景情况:

人工智能 (AI) 在制药开发中越来越重要.
预测人类以太-a-go-go相关基因 (hERG) 通道阻塞对于药物安全至关重要,因为hERG的负担可能导致心脏毒性.

研究的目的:

评估各种机器学习和深度学习模型来预测hERG衍生的毒性.
确定最有效的AI方法来预测新分子结构中的hERG责任.

主要方法:

使用传统的机器学习 (随机森林,SVM,XGBoost,DNN) 和先进的深度学习 (GRU-DNN,GNN) 技术.
在迄今为止最大的hERG数据集上训练和测试模型,包括203,853个用于训练和87,366个用于测试的化合物.
运用图形神经网络 (GNN) 以最小的特征工程和人类干预.

主要成果:

所有评估的模型都表现出强的性能,AUC ROC得分从0.94到0.96.9不等.
图形神经网络 (GNN) 模型实现了最高的预测能力和通用性,AUC ROC得分为0.96.
GNN模型需要最小的特征工程,简化了预测过程.

结论:

先进的AI技术,特别是GNN,对于预测hERG毒性非常有效.
在预测毒理学中,GNN方法促进了全面的自动化,减少了手动干预的需要.
这些人工智能模型是学术机构和制药行业在药物开发早期评估hERG责任的宝贵工具.

关键词:

深度学习 (Deep Learning) 是一种深度学习.图形神经网络是一个图形神经网络.随机的森林随机的森林经常性神经网络的网络.支持矢量机器支持矢量机器赫尔格海峡的运河

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