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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

arXiv 2005.10036 ↗First posted 20 May 2020cs.LG q-bio.QM stat.MLcited by 66

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Abstract (arXiv)

Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five benchmark datasets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple datasets. While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2020.

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Citing papers per year: 2021: 11; 2022: 13; 2023: 14; 2024: 12; 2025: 9; 2026: 7.

66 papers.

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  1. 2021Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classificationcited by 79
  2. 2021Few-shot Conformal Prediction with Auxiliary Taskscited by 53
  3. 2021Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functionscited by 36
  4. 2023Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblescited by 30
  5. 2022Conformal Prediction Sets with Limited False Positivescited by 28
  6. 2021Reliable Graph Neural Networks for Drug Discovery Under Distributional Shiftcited by 22
  7. 2022Fast Uncertainty Estimates in Deep Learning Interatomic Potentialscited by 22
  8. 2021Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networkscited by 21
  9. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  10. 2022Materials Property Prediction with Uncertainty Quantification: A Benchmark Studycited by 14
  11. 2023Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivitycited by 13
  12. 2023Conformal Drug Property Prediction with Density Estimation under Covariate Shiftcited by 13
38 more
  1. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  2. 2022Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfallscited by 11
  3. 2024Kermut: Composite kernel regression for protein variant effectscited by 11
  4. 2022Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUScited by 10
  5. 2023Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forcescited by 10
  6. 2022Self-focusing virtual screening with active design space pruningcited by 8
  7. 2023Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Searchcited by 8
  8. 2024Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNNcited by 8
  9. 2022Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear Systemcited by 7
  10. 2021An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictionscited by 6
  11. 2022Uncertainty-Aware Mixed-Variable Machine Learning for Materials Designcited by 6
  12. 2023Pareto Optimization to Accelerate Multi-Objective Virtual Screeningcited by 6
  13. 2023Validation of uncertainty quantification metrics: a primer based on the consistency and adaptivity conceptscited by 5
  14. 2023Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networkscited by 5
  15. 2024Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labelscited by 5
  16. 2025Molecular Machine Learning in Chemical Process Designcited by 5
  17. 2024Ranking over Regression for Bayesian Optimization and Molecule Selectioncited by 4
  18. 2021Quantifying sources of uncertainty in drug discovery predictions with probabilistic modelscited by 3
  19. 2022Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoringcited by 3
  20. 2024LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fieldscited by 3
  21. 2023Band-gap regression with architecture-optimized message-passing neural networkscited by 2
  22. 2023Evaluating the diversity and utility of materials proposed by generative modelscited by 2
  23. 2024Enhanced sampling of robust molecular datasets with uncertainty-based collective variablescited by 2
  24. 2024Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Designcited by 2
  25. 2025Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scorescited by 2
  26. 2026Quantifying Epistemic Uncertainty in Diffusion Modelscited by 2
  27. 2021An In-depth Summary of Recent Artificial Intelligence Applications in Drug Designcited by 1
  28. 2023Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screeningcited by 1
  29. 2024Reliable OOD Virtual Screening with Extrapolatory Pseudo-Label Matchingcited by 1
  30. 2025Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Modelscited by 1
  31. 2025Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantificationcited by 1
  32. 2025QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarkingcited by 1
  33. 2026Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentialscited by 1
  34. 2026A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Eracited by 1
  35. 2021Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss functioncited by 0
  36. 2021Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Upcited by 0
  37. 2022Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscositycited by 0
  38. 2022Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulationcited by 0

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