| Autor: |
Austen Bernardi, W. F. Drew Bennett, Stewart He, Derek Jones, Dan Kirshner, Brian J. Bennion, Timothy S. Carpenter |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Membranes, Vol 13, Iss 11, p 851 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2077-0375 |
| DOI: |
10.3390/membranes13110851 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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