ArticleMolecular diversity2018
ADME properties evaluation in drug discovery: in silico prediction of blood-brain partitioning.
Article in Molecular diversity, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.Molecules (Basel, Switzerland) · 2020Pooled it
- Safety, Sedative, and Anxiolytic Evaluation of MECS Hard Capsules Using In Silico and In Vivo Approaches.ACS omega · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Phytochemical Profiling and Neuropharmacological Evaluation of Baccaurea motleyana Bark Extract: In Vivo and In Silico Approaches.TheScientificWorldJournal · 2026Article
- Computational Design of Drugs for Epilepsy using a Novel Guided Evolutionary Algorithm for Enhanced Blood Brain Barrier Permeability.Central nervous system agents in medicinal chemistry · 2026Article
- Integrative Profiling for BBB Permeability Using Capillary Electrochromatography, Experimental Physicochemical Parameters, and Ensemble Machine Learning.International journal of molecular sciences · 2025Article
- Design, Synthesis, and Evaluation of Novel (-)-cis-N-Normetazocine Derivatives: In Vitro and Molecular Modeling Insights.Chemical biology & drug design · 2024Article
- Article
- Aniline Derivatives Containing 1-Substituted 1,2,3-Triazole System as Potential Drug Candidates: Pharmacokinetic Profile Prediction, Lipophilicity Analysis Using Experimental and In Silico Studies.Pharmaceuticals (Basel, Switzerland) · 2024Article
- A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches.Current topics in medicinal chemistry · 2024Review
- In Vitro Cytotoxicity of Fluorinated Quaternary Ammonium Salts in Colorectal Cancer Cells and In Silico Pharmacology.Advances in pharmacological and pharmaceutical sciences · 2024Article
- Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models.The AAPS journal · 2023Article
- Selection of potential natural compounds for poly-ADP-ribose polymerase (PARP) inhibition in glioblastoma therapy by in silico screening methods.Saudi journal of biological sciences · 2023Article
- Exploration of the effect of Celastrol on protein targets in nasopharyngeal carcinoma: Network pharmacology, molecular docking and experimental evaluations.Frontiers in pharmacology · 2022Article
- Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients.Frontiers in chemistry · 2021Article
- Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR.Molecular informatics · 2019Article
- Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders.Marine drugs · 2019Article
- Identification of novel inhibitor targeting Fyn kinase using molecular docking analysis.Bioinformation · 2019Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
The absorption, distribution, metabolism and excretion properties are important for drugs, and prediction of these properties in advance will save the cost of drug discovery substantially. The ability to penetrate the blood-brain barrier is critical for drugs targeting central nervous system, which is represented by the ratio of its concentration in brain and in blood. Herein, a quantitative structure-property relationship study was carried out to predict blood-brain partitioning coefficient (logBB) of a data set consisting of 287 compounds. Four different methods including support vector machine, multivariate linear regression, multivariate adaptive regression splines and random forest were employed to build prediction models with 116 molecular descriptors selected by Boruta algorithm. The RF model had best performance in training set ([Formula: see text] = 0.938), test set ([Formula: see text] = 0.840) and tenfold cross-validation ([Formula: see text] = 0.788). Finally, we found that the polar surface area and octanol-water partition coefficient have the greatest influence on blood-brain partitioning. Results suggest that the proposed model is a useful and practical tool to predict the logBB values of drug candidates.
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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.