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- W4319348489 abstract "The chapter outlines how both supervised and unsupervised machine learning approaches, built with chemical descriptors, are used to develop models that provide actionable insights to support drug safety prediction. The process of preparing a data set, containing both chemical structures and toxicity data, for use with machine learning algorithms is described. How structural features, scaffolds, and other descriptors are derived from these chemicals structures is outlined alongside how these descriptors are used in a variety of machine learning algorithms to build predictive models and expert alert-based systems. A practical case study is presented showing how these computational approaches support the regulatory assessment of pharmaceutical impurities." @default.
- W4319348489 created "2023-02-08" @default.
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- W4319348489 date "2023-01-01" @default.
- W4319348489 modified "2023-09-27" @default.
- W4319348489 title "The Use of Machine Learning to Support Drug Safety Prediction" @default.
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- W4319348489 doi "https://doi.org/10.1007/978-3-031-20730-3_16" @default.
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