ArticleBioinformatics (Oxford, England)2023
A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
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- Dual Targeted Far-Red Emissive Small Molecules for Mitochondrial Imaging and Multifunctional Modulation in Alzheimer's Disease.Advanced healthcare materials · 2026Article
- Overrepresentation Bias Leads to Performance Overestimation in Blood-Brain Barrier Permeability Prediction Models: Characterization and Mitigation.Journal of chemical information and modeling · 2026Article
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- Identification of low-toxicity DNA topoisomerase I inhibitors with potential blood-brain barrier penetrability for glioblastoma therapy: structure-based virtual screening reveals promising novel Scaffolds.Molecular diversity · 2026Article
- Comparison of In Vitro Metrics With Real-World Risk of Drug-Induced Parkinsonism Due to Antipsychotic Drugs: Retrospective Cohort Study.JMIR public health and surveillance · 2026Article
- SU-Eohyeol Pharmacopuncture Ameliorates Parkinson's Disease-Associated Pain via the CB1 and PPARγ Pathways in an MPTP-Induced Mouse Model.Pain research & management · 2026Article
- Engineering Nanocarriers for Dopamine Stabilization and Targeted Brain Delivery: Mechanisms, Approaches and Translational Challenges.International journal of nanomedicine · 2026Review
- Animal Venoms as Peptide Libraries for the Discovery of Antiglioblastoma Agents.Biochemistry research international · 2026Review
- Deep learning identifies TP-41 for methylglyoxal scavenging in Alzheimer's treatment.Theranostics · 2026Article
- BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction.CNS & neurological disorders drug targets · 2026Article
- Integrative Profiling for BBB Permeability Using Capillary Electrochromatography, Experimental Physicochemical Parameters, and Ensemble Machine Learning.International journal of molecular sciences · 2025Article
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10 authors.
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Abstract
motivationEfficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.
resultsHere, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates. AVAILABILITY AND IMPLEMENTATION: Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.
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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.