In silico ADMT prediction
Abstract
In silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction has transformed drug discovery by providing an opportunity to assess pharmacokinetic and toxicological activity in early development stages at a very low cost and short development time. This chapter gives a broad picture of computational processes used in ADMET prediction with a focus on the purpose of these techniques to rank drug candidates and reduce late-phase failures. Starting with theoretical aspects, we examine the idea of molecular descriptors, Quantitative Structure-Property Relationships (QSPR), and how they can be combined with systems biology. The chapter explores the methods of computation, such as rule-based models (e.g., the Rule of Five by Lipinski), machine learning (ML) (e.g., deep neural networks), and computational models of molecules, such as docking and dynamics simulations. All the ADMET parameters are discussed, including ADMET with an emphasis on predictive models of bioavailability, blood-brain barrier (BBB) penetration, cytochrome P450, clearance rates and organ tissue toxicity. Tools such as SwissADME, Schrodinger Suite, databases (e.g., ChEMBL, PubChem) and validation strategies (e.g., cross-validation, applicability domain analysis) are reviewed. The practical uses of virtual screening, optimizing leads, and repurposing drugs are described with the help of case studies of successful predictions. Issues of data quality, model generalizability, and…
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