ArticleJournal of cheminformatics2023
Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bridging between Structure-Based and Data-Driven Affinity Prediction.Journal of chemical information and modeling · 2026Article
- Query Matters: How Selection Strategies Influence Active Learning in Drug Discovery.Journal of chemical information and modeling · 2026Article
- A simple compound prioritization method for drug discovery considering multi-target binding.Digital discovery · 2026Article
- Explicit Applicability Domain Calculations Can Help Determine When Uncertainty Estimates Are Less Reliable.ACS omega · 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
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market.Nature communications · 2026Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Uncertainty quantification with graph neural networks for efficient molecular design.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
4 authors.
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Abstract
Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure-property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.
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