ArticlePharmaceutical research2008
New predictive models for blood-brain barrier permeability of drug-like molecules.
Article in Pharmaceutical research, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds.Molecules (Basel, Switzerland) · 2020Pooled it
- BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction.CNS & neurological disorders drug targets · 2026Article
- Feature-Guided Machine Learning for Studying Passive Blood-Brain Barrier Permeability to Aid Drug Discovery.International journal of molecular sciences · 2025Article
- Employing Automated Machine Learning (AutoML) Methods to Facilitate theJournal of chemical information and modeling · 2025Article
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- Exploring blood-brain barrier passage using atomic weighted vector and machine learning.Journal of molecular modeling · 2024Article
- Development of QSAR models to predict blood-brain barrier permeability.Frontiers in pharmacology · 2022Article
- The Application ofFrontiers in nuclear medicine · 2022Review
- Predicting Drug Blood-Brain Barrier Penetration with Adverse Event Report Embeddings.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors.Scientific data · 2021Article
- Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review.Molecular diversity · 2021Review
- Development of an a priori computational approach for brain uptake of compounds in an insect model system.Bioorganic & medicinal chemistry letters · 2021Article
- A deep learning approach to predict blood-brain barrier permeability.PeerJ. Computer science · 2021Article
- Identification of a Novel Allosteric Modulator of the Human Dopamine Transporter.ACS chemical neuroscience · 2019Article
- Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization.Journal of chemical information and modeling · 2019Article
- Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders.Marine drugs · 2019Article
- Computational systems biology approaches for Parkinson's disease.Cell and tissue research · 2018Review
- The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.Molecular diversity · 2017Article
- Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.Bioinformatics (Oxford, England) · 2017Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
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4 authors.
Funding
Abstract
purposeThe goals of the present study were to apply a generalized regression model and support vector machine (SVM) models with Shape Signatures descriptors, to the domain of blood-brain barrier (BBB) modeling. MATERIALS AND
methodsThe Shape Signatures method is a novel computational tool that was used to generate molecular descriptors utilized with the SVM classification technique with various BBB datasets. For comparison purposes we have created a generalized linear regression model with eight MOE descriptors and these same descriptors were also used to create SVM models.
resultsThe generalized regression model was tested on 100 molecules not in the model and resulted in a correlation r2 = 0.65. SVM models with MOE descriptors were superior to regression models, while Shape Signatures SVM models were comparable or better than those with MOE descriptors. The best 2D shape signature models had 10-fold cross validation prediction accuracy between 80-83% and leave-20%-out testing prediction accuracy between 80-82% as well as correctly predicting 84% of BBB+ compounds (n = 95) in an external database of drugs.
conclusionsOur data indicate that Shape Signatures descriptors can be used with SVM and these models may have utility for predicting blood-brain barrier permeation in drug discovery.
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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.