ArticleChemical science2019
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning.
Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- Out-of-distribution evaluation of active learning pipelines for molecular property prediction.RSC advances · 2026Article
- Ai derivation and exploration of antibiotic class spaces.Journal of cheminformatics · 2026Article
- The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human-AI Symbiosis in Self-Driving Polymer Laboratories.Macromolecular rapid communications · 2025Review
- Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification.Journal of chemical information and modeling · 2025Article
- Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning.Nature communications · 2025Article
- Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design.Journal of cheminformatics · 2025Article
- Uncertainty quantification with graph neural networks for efficient molecular design.Nature communications · 2025Article
- Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification.Journal of imaging informatics in medicine · 2025Article
- Article
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- Reducing overconfident errors in molecular property classification using Posterior Network.Patterns (New York, N.Y.) · 2024Article
- Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.Journal of chemical information and modeling · 2024Article
- Bayesian Optimization in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Finding the most potent compounds using active learning on molecular pairs.Beilstein journal of organic chemistry · 2024Article
- State-of-the-art learning COVID-19 vaccine effectiveness using LSTM.Informatics in medicine unlocked · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery. Deep learning is becoming the method of choice but studies to date focus on mean accuracy as the main metric. However, to replace costly and mission-critical experiments by models, a high mean accuracy is not enough: outliers can derail a discovery campaign, thus models need to reliably predict when it will fail, even when the training data is biased; experiments are expensive, thus models need to be data-efficient and suggest informative training sets using active learning. We show that uncertainty quantification and active learning can be achieved by Bayesian semi-supervised graph convolutional neural networks. The Bayesian approach estimates uncertainty in a statistically principled way through sampling from the posterior distribution. Semi-supervised learning disentangles representation learning and regression, keeping uncertainty estimates accurate in the low data limit and allowing the model to start active learning from a small initial pool of training data. Our study highlights the promise of Bayesian deep learning for chemistry.
Identifiers
What OpenQuestion holds
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.