ArticleJournal of chemical information and modeling2019
Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.
Article in Journal of chemical information and modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed.
- Estimating Permeabilities from Biased Molecular Dynamics with a Green-Kubo Formalism for Transport across the Blood-Brain Barrier.Journal of chemical information and modeling · 2026Article
- Quantifying Small-Molecule Association with Lipid Membranes: Methods, Models, and Limitations.Membranes · 2026Review
- Permeability Benchmarking: Guidelines for ComparingJournal of chemical information and modeling · 2025Review
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Improving the Accuracy of Permeability Data to Gain Predictive Power: Assessing Sources of Variability in Assays Using Cell Monolayers.Membranes · 2024Review
- 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
- A Simulation Model for the Non-Electrogenic Uniport Carrier-Assisted Transport of Ions across Lipid Membranes.Membranes · 2022Article
- Article
- Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Functional Group Distributions, Partition Coefficients, and Resistance Factors in Lipid Bilayers Using Site Identification by Ligand Competitive Saturation.Journal of chemical theory and computation · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Dp44mT, an iron chelator, suppresses growth and induces apoptosis via RORA-mediated NDRG2-IL6/JAK2/STAT3 signaling in glioma.Cellular oncology (Dordrecht, Netherlands) · 2020Article
- Delivery of Alpha-Mangostin Using Cyclodextrins through a Biological Membrane: Molecular Dynamics Simulation.Molecules (Basel, Switzerland) · 2020Article
- Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules.Journal of chemical information and modeling · 2019Article
- PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules.Journal of chemical information and modeling · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.
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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.