ArticleJournal of chemical information and modeling2023
Characterizing Uncertainty in Machine Learning for Chemistry.
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 30 papers.
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Who cites it
30 citing papers in PubMed.
- Computational and Data-Driven Strategies for Lignocellulosic Biomass Valorization: From Multiscale Modeling to Digitalized Biorefinery Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- Exploring the chemical space of transition-metal cluster thermodynamics via automated first-principles calculations and machine learning.Nature communications · 2026Article
- tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.Journal of chemical information and modeling · 2026Article
- A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.Journal of imaging informatics in medicine · 2026Article
- Learning molecular determinants of selective small-molecule partitioning across biomolecular condensates.bioRxiv : the preprint server for biology · 2026Article
- A Leakage-Aware Drug Discovery Workflow for PKM2 and MAPK1 Integrating Scaffold Validation, Molecular Docking and Structural Triage.International journal of molecular sciences · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Using a Hierarchical Molecular Structure-Based Method to Estimate the Physicochemical Properties of Halogen/Cyano-Substituted Alkanes.ACS omega · 2026Article
- Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts.Journal of chemical information and modeling · 2026Article
- Integrating generative AI and physical tests to strengthen claims about emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Uncertainty in the era of machine learning for atomistic modeling.Digital discovery · 2025Review
- Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.Journal of chemical theory and computation · 2025Article
- Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification.Journal of chemical information and modeling · 2025Article
- Data-driven organic solubility prediction at the limit of aleatoric uncertainty.Nature communications · 2025Article
- How to crack a SMILES: automatic crosschecked chemical structure resolution across multiple services using MoleculeResolver.Journal of cheminformatics · 2025Article
- Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.Chemical research in toxicology · 2025Article
- Uncertainty quantification with graph neural networks for efficient molecular design.Nature communications · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.Chemical science · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and shortcomings of the model (epistemic), further dividing epistemic uncertainty into model bias and variance contributions. We systematically address the influence of noise, model bias, and model variance in the context of chemical property predictions, where the diverse nature of target properties and the vast chemical chemical space give rise to many different distinct sources of prediction error. We demonstrate that different sources of error can each be significant in different contexts and must be individually addressed during model development. Through controlled experiments on data sets of molecular properties, we show important trends in model performance associated with the level of noise in the data set, size of the data set, model architecture, molecule representation, ensemble size, and data set splitting. In particular, we show that 1) noise in the test set can limit a model's observed performance when the actual performance is much better, 2) using size-extensive model aggregation structures is crucial for extensive property prediction, and 3) ensembling is a reliable tool for uncertainty quantification and improvement specifically for the contribution of model variance. We develop general guidelines on how to improve an underperforming model when falling into different uncertainty contexts.
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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.