ArticleJournal of cheminformatics2022
Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Explicit Applicability Domain Calculations Can Help Determine When Uncertainty Estimates Are Less Reliable.ACS omega · 2026Article
- Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery.Journal of chemical information and modeling · 2024Article
- The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research · 2024Article
- Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors.Beilstein journal of nanotechnology · 2024Article
- Practical guidelines for the use of gradient boosting for molecular property prediction.Journal of cheminformatics · 2023Article
- Large-Scale Modeling of Sparse Protein Kinase Activity Data.Journal of chemical information and modeling · 2023Article
- EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.Nucleic acids research · 2023Article
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Authors and funding
5 authors.
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Abstract
Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are able to use experimental activity values for auxiliary assays when predicting the activity of a test compound on a specific assay. In this study, we tested three different multi-task imputation techniques on three classification-based toxicity datasets: two of small scale (12 assays each) and one large scale with 417 assays. Moreover, we analyzed in detail the improvements shown by the imputation models. We found that test compounds that were dissimilar to training compounds, as well as test compounds with a large number of experimental values for other assays, showed the largest improvements. We also investigated the impact of sparsity on the improvements seen as well as the relatedness of the assays being considered. Our results show that even a small amount of additional information can provide imputation methods with a strong boost in predictive performance over traditional single task and multi-task predictive models.
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