ArticleMarine drugs2019
Predicting Blood⁻Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders.
Article in Marine drugs, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Targeting Kinase Signaling in Glioblastoma: Structural Optimization, Blood-Brain Barrier Dynamics and Combinatorial Translational Strategies.International journal of molecular sciences · 2026Review
- Discovery of a Novel and Potent Kir4.1 Inhibitor as a Safe and Rapid-Onset Antidepressant Agent in Mice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Next Generation DNA Damage Response Inhibitors: Harnessing Nanocarriers and Tumor Microenvironment for Precision Cancer Therapy.Oncology research · 2026Review
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- 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
- Improving glioma drug delivery: A multifaceted approach for glioma drug development.Pharmacological research · 2024Review
- Predicting blood-brain barrier permeability of molecules with a large language model and machine learning.Scientific reports · 2024Article
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Journal of computer-aided molecular design · 2023Article
- Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery.Membranes · 2023Review
- A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.Bioinformatics (Oxford, England) · 2023Article
- Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.Journal of chemical information and modeling · 2023Article
- [Resampling combined with stacking learning for prediction of blood-brain barrier permeability of compounds].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Cutting-edge advances in modeling the blood-brain barrier and tools for its reversible permeabilization for enhanced drug delivery into the brain.Cell & bioscience · 2023Review
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Research square · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.International journal of molecular sciences · 2023Review
- Improving VAE based molecular representations for compound property prediction.Journal of cheminformatics · 2022Article
- Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules.Bioinformatics (Oxford, England) · 2022Article
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
Corrections and comments
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Authors and funding
2 authors.
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Abstract
The recent success of small-molecule kinase inhibitors as anticancer drugs has generated significant interest in their application to other clinical areas, such as disorders of the central nervous system (CNS). However, most kinase inhibitor drug candidates investigated to date have been ineffective at treating CNS disorders, mainly due to poor blood⁻brain barrier (BBB) permeability. It is, therefore, imperative to evaluate new chemical entities for both kinase inhibition and BBB permeability. Over the last 35 years, marine biodiscovery has yielded 471 natural products reported as kinase inhibitors, yet very few have been evaluated for BBB permeability. In this study, we revisited these marine natural products and predicted their ability to cross the BBB by applying freely available open-source chemoinformatics and machine learning algorithms to a training set of 332 previously reported CNS-penetrant small molecules. We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated model accuracies of 80%⁻82% and 78%⁻80% on external testing. All 3 binary classifiers predicted 13 marine-derived kinase inhibitors with appropriate physicochemical characteristics for BBB permeability.
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