ArticleJournal of cheminformatics2022
Blood-brain barrier penetration prediction enhanced by uncertainty estimation.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 42 citations in OpenAlex.
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Microneedle technology in the treatment of ocular diseases: Advances and applications.Asian journal of pharmaceutical sciences · 2026Review
- Geometry-based BERT: An experimentally validated deep learning model for molecular property prediction in drug discovery.Journal of pharmaceutical analysis · 2025Article
- Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach.Journal of pharmaceutical analysis · 2025Article
- Advances in brain-targeted delivery strategies and natural product-mediated enhancement of blood-brain barrier permeability.Journal of nanobiotechnology · 2025Review
- Exosome-powered neuropharmaceutics: unlocking the blood-brain barrier for next-gen therapies.Journal of nanobiotechnology · 2025Review
- GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network.The AAPS journal · 2025Article
- AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.Journal of cheminformatics · 2024Article
- Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models.ACS chemical neuroscience · 2024Article
- Non-animal models for blood-brain barrier permeability evaluation of drug-like compounds.Scientific reports · 2024Article
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules.Molecules (Basel, Switzerland) · 2024Review
- Screening, optimization, and ADMET evaluation of HCJ007 for pancreatic cancer treatment through active learning and dynamics simulation.Frontiers in chemistry · 2024Article
- Association between aromatic amines and serum neurofilament light chain as a biomarker of neural damage: a cross-sectional study from NHANES.Frontiers in public health · 2024Article
- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.Nature communications · 2023Article
- Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.Molecules (Basel, Switzerland) · 2023Review
- Toward the use of novel alternative methods in epilepsy modeling and drug discovery.Frontiers in neurology · 2023Review
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
Blood-brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood-brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB + from BBB - to above 99% by extracting predictions with high confidence level (uncertainty score < 0.1). Case studies on preclinical/clinical drugs for Alzheimer' s disease and marketed antitumor drugs that verified by literature proved the application value of uncertainty estimation enhanced BBBp prediction model, that may facilitate the drug discovery in the field of CNS diseases and metastatic brain tumors.
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Registered trials
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.