Citation:

Fourches, D., A. Williams, G. Patlewicz, I. Shah, Chris Grulke, J. Wambaugh, A. Richard, AND A. Tropsha. Computational tools for ADMET profiling. Chapter 8, Computational Toxicology: Risk Assessment for Chemicals. John Wiley & Sons Inc, Malden, MA, , 211-244, (2018). https://doi.org/10.1002/9781119282594.ch8

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Book chapter -- Computational tools for ADMET profiling

Description:

The knowledge of ADMET (absorption, distribution, metabolism, excretion and toxicity) properties is critical for the rapid progression of new molecular entities from hits to drugs. Public repositories of measured ADMET properties have been growing rapidly in recent years facilitating the possibility of developing impactful ADMET prediction models. However, data growth and availability alone do not guarantee improved modeling outcomes. There is also a need to ensure rigorous data processing and modeling tools. In this chapter, we discuss (i) the importance of standardized protocols and approaches (e.g., chemical curation, modelability index, hybrid QSAR modeling, chemical read-across) and how they can be applied to obtaining externally predictpredictive ADMET models, (ii) the potential utility of these models for regulatory purposes, particularly in the context of supporting mechanistic elements, as captured within Adverse Outcomes Pathways (AOPs,) (iii) main challenges (e.g., biological curation, activity cliffs, multi-endpoint modeling, in vitro/in vivo continuum, interpretability and barriers to implementation) still facing the current generation of cheminformatics methods, and (iv) perspectives on the development of the next generation of computational ADMET predictors (e.g., accessibility with mobile devices, metabolite profiling, structure-exposure relationships).

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