ArticleACS chemical neuroscience2024
Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.
Article in ACS chemical neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Biomedical Materials and Fabrication Methods for Construction of In Vitro Neurovascular Unit Models.Materials (Basel, Switzerland) · 2026Review
- Design, preclinical evaluation, and multicenter phase 1 clinical study of HZ-A-018 for relapsed or refractory central nervous system lymphoma.Acta pharmaceutica Sinica. B · 2026Article
- Article
- Dioxopiperidinamide Derivative SKT-36 Alleviates scopolamine-induced Cognitive and Neurobehavioral Impairments in an In-vivo Zebrafish Model.Cell biochemistry and biophysics · 2026Article
- Flavonoids, Chalcones, and Their Fluorinated Derivatives-Recent Advances in Synthesis and Potential Medical Applications.Molecules (Basel, Switzerland) · 2025Review
- Toxic Alerts of Endocrine Disruption Revealed by Explainable Artificial Intelligence.Environment & health (Washington, D.C.) · 2025Article
- Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges.ACS applied materials & interfaces · 2025Review
- Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.Molecular informatics · 2025Review
- High throughput screening identifies potential inhibitors targeting trimethoprim resistant DfrA1 protein in Klebsiella pneumoniae and Escherichia coli.Scientific reports · 2025Article
- Prediction of the Extent of Blood-Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model.Pharmaceutical research · 2025Article
- Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.Journal of chemical information and modeling · 2024Article
- Synthetic Approaches, Properties, and Applications of Acylals in Preparative and Medicinal Chemistry.Molecules (Basel, Switzerland) · 2024Review
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Authors and funding
4 authors.
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Abstract
The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.
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