In the drug development process, mouse liver microsomal (MLM) studies are an invaluable biological assay used to assess the metabolic stability of novel drug candidates prior to human studies. However, determining MLM stability, in addition to other absorption, distribution, metabolism, and excretion (ADME) properties, can be a time‐intensive and expensive process if it were tested in many compounds, thus leading to the need to create computational models capable of predicting properties of novel compounds. Additionally, building accurate computational models for the prediction of MLM stability can greatly accelerate the screening process for the selection of an appropriate drug candidate and further reduce the failure rate of the compounds in later trial stages. Our study outlined within this paper will discuss the history of computational models and their ability to predict MLM stability using traditional machine learning methods, as well as discuss a novel deep learning architecture, graph convolutional neural networks, capable of stronger predictive capabilities when compared to traditional methods. With future advances in hardware and research, deep learning methods applied to the prediction of ADME properties including but not limited to microsomal stability prediction represent an invaluable tool for future drug discovery efforts in both industry and academic settings.

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预测小鼠肝微粒体研究的进展：从机器学习到深度学习

在药物开发过程中，小鼠肝微粒体（MLM）研究是一种宝贵的生物学测定方法，用于评估人类研究之前新候选药物的代谢稳定性。但是，如果要在许多化合物中进行测试，则除了要具有其他吸收，分布，代谢和排泄（ADME）特性外，确定MLM的稳定性可能是一个耗时且昂贵的过程，因此需要创建具有以下功能的计算模型：新化合物的预测性能 此外，建立用于预测MLM稳定性的准确计算模型可以极大地加快筛选过程，以选择合适的候选药物，并进一步降低化合物在后续试验阶段的失败率。本文概述的研究将讨论计算模型的历史及其使用传统机器学习方法预测MLM稳定性的能力，并讨论一种新型的深度学习体系结构，图卷积神经网络，与传统方法相比具有更强的预测能力方法。随着硬件和研究的未来发展，应用于ADME特性预测的深度学习方法（包括但不限于微粒体稳定性预测）代表了未来在工业和学术环境中发现药物的宝贵工具。与传统方法相比，具有更强的预测能力。随着硬件和研究的未来发展，应用于ADME特性预测的深度学习方法包括但不限于微粒体稳定性预测，代表了工业和学术环境中未来药物发现工作的宝贵工具。与传统方法相比，具有更强的预测能力。随着硬件和研究的未来发展，应用于ADME特性预测的深度学习方法（包括但不限于微粒体稳定性预测）代表了未来在工业和学术环境中发现药物的宝贵工具。