ReviewMembranes2023
Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery.
Review in Membranes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.Chemical biology & drug design · 2026Review
- Biopharmaceutical Profiling of Herpetrione: A BCS II Properties and P-gp Substrate Guiding Nanoparticle Design.Pharmaceutics · 2026Article
- Article
- Physiologically Based Pharmacokinetic (PBPK) Modeling of Oral Drug Absorption for Integrating Nonclinical Data into Human Pharmacokinetic Predictions.The AAPS journal · 2026Review
- Gradient Descent to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Virtual screening and MD simulation-guided discovery of CDK12-IN-3, a potent ALK inhibitor suppressing neuroblastoma growthFrontiers in chemistry · 2026Article
- Prodrug Approach as a Strategy to Enhance Drug Permeability.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Overcoming Challenges in Small-Molecule Drug Bioavailability: A Review of Key Factors and Approaches.International journal of molecular sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
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.