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WIREs Comput Mol Sci
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Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning

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Abstract 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. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming
Deep learning models enact automatic feature selection, an advantage over traditional machine models requiring (often unreliable) manual extraction. The “Classification” represents actions to train models and classify compounds based on traditional machine learning methods. We exhibited the traditional neural network method as an example here for comparison of deep learning
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Architecture of graph convolution networks
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Architecture of traditional decision tree models
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Computer and Information Science > Computer Algorithms and Programming
Computer and Information Science > Chemoinformatics

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