ArticleFrontiers in neuroscience2022
DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- From Design to Bedside: Brain-Targeted Engineered Nanoparticles and Predictive Modeling for Neurodegenerative Diseases Therapeutics.Pharmaceutical research · 2026Review
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.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
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- Overrepresentation Bias Leads to Performance Overestimation in Blood-Brain Barrier Permeability Prediction Models: Characterization and Mitigation.Journal of chemical information and modeling · 2026Article
- Leveraging quantum chemical properties in transfer learning for predicting blood-brain barrier permeability of drugs.Drug delivery and translational research · 2026Article
- Peptide-functionalized nanoparticles for brain-targeted therapeutics.Drug delivery and translational research · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Identification of potent high-affinity secondary nucleation inhibitors of Aβ42 aggregation from an ultra-large chemical library using deep docking.Molecular systems biology · 2026Article
- BBBper: A Machine Learning-based Online Tool for Blood-brain Barrier (BBB) Permeability Prediction.CNS & neurological disorders drug targets · 2026Article
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- Beyond amyloid: nanobody-mediated neuroinflammatory therapy for Alzheimer's disease.Translational neurodegeneration · 2025Review
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Exploring Oxazolidinone scaffolds for future antibiotics: synthesis and computational insights with DFT, docking, ADME and MD simulation.Journal of computer-aided molecular design · 2025Article
- Integrated machine learning and deep learning-based virtual screening framework identifies novel natural GSK-3β inhibitors for Alzheimer's disease.Journal of computer-aided molecular design · 2025Article
- 3BTRON: A Blood-Brain Barrier Recognition Network.Communications biology · 2025Article
- Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.Scientific reports · 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
- Prodrug Approach as a Strategy to Enhance Drug Permeability.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
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6 authors.
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Abstract
The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the "DeePred-BBB" model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.
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