ArticleBioMed research international2015
A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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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
19 citing papers in PubMed.
- DeepBBB: A Data-Composition-Aware Graph Screening Workflow for BBB-Focused CNS Library Construction and Prospective PAMPA-BBB Evaluation.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Determinants and Machine Learning Prediction of Subtherapeutic Sodium Valproate Concentrations in Epilepsy Management in Xinjiang, China.European journal of drug metabolism and pharmacokinetics · 2026Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- A Classification-Based Blood-Brain Barrier Model: A Comparative Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.Journal of chemical information and modeling · 2025Article
- Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier.Pharmaceuticals (Basel, Switzerland) · 2025Review
- The penetration of therapeutics across the blood-brain barrier: Classic case studies and clinical implications.Cell reports. Medicine · 2024Review
- Article
- Development of QSAR models to predict blood-brain barrier permeability.Frontiers in pharmacology · 2022Article
- DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy.Frontiers in neuroscience · 2022Article
- Dynamic 3D On-Chip BBB Model Design, Development, and Applications in Neurological Diseases.Cells · 2021Review
- Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model.ACS chemical neuroscience · 2021Article
- Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model.BioMed research international · 2021Article
- Natural Approaches for Neurological Disorders-The Neuroprotective Potential ofMolecules (Basel, Switzerland) · 2020Article
- Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.Journal of radiation research · 2019Article
- Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning.Scientific reports · 2019Article
- Blood Brain Barrier and Alzheimer's Disease: Similarity and Dissimilarity of Molecular Alerts.Current neuropharmacology · 2018Review
- A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.Journal of pathology informatics · 2018Article
- Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.
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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.