ArticleJournal of chemical information and modeling2023
Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- ADMET-XSpec: A Tool for Systematic Cross-Species Data Integration in ADMET Prediction.Chemical research in toxicology · 2026Article
- PolyT-GNN: A Graph Neural Network Framework for Data-Driven Discovery of High-Temperature Two-Way Shape Memory Polymers.ACS applied materials & interfaces · 2026Article
- Oral bioavailability property prediction based on task similarity transfer learning.Molecular diversity · 2026Article
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- Polymer Drug Conjugate: A Revolution in Drug Delivery.AAPS PharmSciTech · 2026Review
- Review
- Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics.Yonago acta medica · 2026Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Application of directed message-passing neural network to predict human oral bioavailability of pharmaceuticals.Journal of computer-aided molecular design · 2025Article
- Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction.Molecules (Basel, Switzerland) · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pre-training a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.
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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.