ArticlePatterns (New York, N.Y.)2024
Reducing overconfident errors in molecular property classification using Posterior Network.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Learning-Aided Drug Repurposing for Screening COX-2 Inhibitors from Traditional Chinese Medicines.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Review
- Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach.Journal of pharmaceutical analysis · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models.Journal of cheminformatics · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep-learning-based classification models are increasingly used for predicting molecular properties in drug development. However, traditional classification models using the Softmax function often give overconfident mispredictions for out-of-distribution samples, highlighting a critical lack of accurate uncertainty estimation. Such limitations can result in substantial costs and should be avoided during drug development. Inspired by advances in evidential deep learning and Posterior Network, we replaced the Softmax function with a normalizing flow to enhance the uncertainty estimation ability of the model in molecular property classification. The proposed strategy was evaluated across diverse scenarios, including simulated experiments based on a synthetic dataset, ADMET predictions, and ligand-based virtual screening. The results demonstrate that compared with the vanilla model, the proposed strategy effectively alleviates the problem of giving overconfident but incorrect predictions. Our findings support the promising application of evidential deep learning in drug development and offer a valuable framework for further research.
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