ArticleMolecules (Basel, Switzerland)2021
Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- 7α-acetoxy-6β-hydroxyroyleanone (Roy) modulates IL-6/STAT3/JAK2 mRNA expression and suppresses tumor growth in glioblastoma cell models.Frontiers in pharmacology · 2026Article
- Article
- Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction.PloS one · 2025Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules.Molecules (Basel, Switzerland) · 2024Review
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.BioMedicine · 2024Review
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Journal of computer-aided molecular design · 2023Article
- The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases.Research square · 2023Article
- Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.Nature communications · 2023Article
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3 authors.
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Abstract
The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form and in-blood-form datasets were prepared by modifying the original BBBP dataset, and the effects of the data modification were investigated. For each dataset, molecular descriptors were generated and used for BBBP prediction by machine learning (ML). For ML, the dataset was split into training, validation, and test data by the scaffold split algorithm MoleculeNet used. This creates an unbalanced split and makes the prediction difficult; however, we decided to use that algorithm to evaluate the predictive performance for unknown compounds dissimilar to existing ones. The highest prediction score was obtained by the random forest model using 212 descriptors from the free-form dataset, and this score was higher than the existing best score using the same split algorithm without using any external database. Furthermore, using a deep neural network, a comparable result was obtained with only 11 descriptors from the free-form dataset, and the resulting descriptors suggested the importance of recognizing the glucose-like characteristics in BBBP prediction.
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