Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 25, 2023
- Source ID
- 10.3390/membranes13110851
Entities
People
- Austen Bernardi
- Brian J. Bennion
- Dan Kirshner
- Derek Jones
- Drew Bennett
- Stewart He
- Timothy S Carpenter
Organizations
- Defense Threat Reduction Agency
- Lawrence Livermore National Laboratory