ArticleThe AAPS journal2018
Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.
Article in The AAPS journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 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
- A‑iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework.ACS medicinal chemistry letters · 2026Article
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Advances in Machine Learning-Enhanced PBPK Models for Brain-Targeted Drug Delivery via Nanocarriers: A Comprehensive Review.Journal of functional biomaterials · 2026Review
- Blood‒brain barrier permeability assessment: from biomimetic models to multimodal imaging.Journal of Zhejiang University. Science. B · 2026Review
- Machine learning-driven prioritization and experimental validation of traditional Chinese medicine-derived STING-inhibitory candidates.Molecular diversity · 2026Article
- Leveraging quantum chemical properties in transfer learning for predicting blood-brain barrier permeability of drugs.Drug delivery and translational research · 2026Article
- Computational Evaluation of Potential c-Abl Kinase Inhibitors for Parkinson's Disease: QSAR, Docking, Bioisosteric Replacement, ADMET, and MD Simulations.Applied biochemistry and biotechnology · 2025Article
- Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.AAPS PharmSciTech · 2025Review
- Synthesis, Characterization, and Biological Evaluation of Novel Azo-Based Imidazo[1,2‑ACS omega · 2025Article
- 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
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.Molecular informatics · 2025Review
- Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood-Brain Barrier.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Neurotherapeutics across blood-brain barrier: screening of BBB-permeable and CNS-active molecules for neurodegenerative disease.Frontiers in pharmacology · 2025Article
- Synthesis, crystal structure, and antiviral evaluation of new imidazopyridine-schiff base derivatives:RSC advances · 2024Article
- Combined machine learning models, docking analysis, ADMET studies and molecular dynamics simulations for the design of novel FAK inhibitors against glioblastoma.BMC chemistry · 2024Article
- Improving glioma drug delivery: A multifaceted approach for glioma drug development.Pharmacological research · 2024Review
- Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.
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