ArticleJournal of chemical information and modeling2012
Testing physical models of passive membrane permeation.
Article in Journal of chemical information and modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
39 citing papers in PubMed.
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- In Silico Prediction and Validation of the Permeability of Small Molecules Across the Blood-Brain Barrier.International journal of molecular sciences · 2026Article
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- Evaluation of Pregabalin bioadhesive multilayered microemulsion IOP-lowering eye drops.Journal of controlled release : official journal of the Controlled Release Society · 2024Article
- Synthesis and Characterization of Folic Acid-Conjugated Terbium Complexes as Luminescent Probes for Targeting Folate Receptor-Expressing Cells.Journal of medicinal chemistry · 2024Article
- Deepening insights into cholinergic agents for intraocular pressure reduction: systems genetics, molecular modeling, andFrontiers in molecular biosciences · 2024Article
- Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.Journal of chemical information and modeling · 2023Article
- Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.Journal of chemical information and modeling · 2023Review
- Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design.Current opinion in structural biology · 2023Review
- Commercially Available Cell-Free Permeability Tests for Industrial Drug Development: Increased Sustainability through Reduction of In Vivo Studies.Pharmaceutics · 2023Review
- Permeability of New Antifungal Fluconazole Derivatives through a Lipophilic Membrane: Experiment and Modeling.Molecules (Basel, Switzerland) · 2023Article
- Identification of Polyphenol Derivatives as Novel SARS-CoV-2 and DENV Non-Nucleoside RdRp Inhibitors.Molecules (Basel, Switzerland) · 2022Article
- Structure-based discovery of nonopioid analgesics acting through the αScience (New York, N.Y.) · 2022Article
- Stress impact of liposomes loaded with ciprofloxacin on the expression level of MepA and NorB efflux pumps of methicillin-resistant Staphylococcus aureus.International microbiology : the official journal of the Spanish Society for Microbiology · 2022Article
- Palladium(II) Complexes of Substituted Salicylaldehydes: Synthesis, Characterization and Investigation of Their Biological Profile.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Crossing of the Cystic Barriers ofMolecules (Basel, Switzerland) · 2021Article
- Predicting Absorption-Distribution Properties of Neuroprotective Phosphine-Borane Compounds Using In Silico Modeling and Machine Learning.Molecules (Basel, Switzerland) · 2021Article
- Benchmark of Generic Shapes for Macrocycles.Journal of chemical information and modeling · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.