Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Bernardi, Austen; Bennett, W. F. Drew; He, Stewart; Jones, Derek; Kirshner, Dan; Bennion, Brian J.; Carpenter, Timothy S.

doi:10.3390/membranes13110851

Title: Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Journal Article · 24 October 2023 · Membranes

DOI: https://doi.org/10.3390/membranes13110851 · OSTI ID:2203569

;

; He, Stewart; Jones, Derek; Kirshner, Dan; Bennion, Brian J.; Carpenter, Timothy S.

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2203569

Alternate ID(s):: OSTI ID: 2280471

Journal Information:: Membranes, Journal Name: Membranes Journal Issue: 11 Vol. 13; ISSN 2077-0375; ISSN MBSEB6

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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