Journal Article

Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction

Gillet, Valerie J., Allen, Luke N., Webb, Samuel J., Walter, Moritz, de la Vega de León, Antonio

2022

Overview

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.

• Publisher: Springer International Publishing,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Analysis / Assaying / Biocompatibility / Biological activity / Chemistry / Chemistry and Materials Science / Computational Biology/Bioinformatics / Computer Applications in Chemistry / Datasets / Documentation and Information in Chemistry / Imputation modeling / Model evaluation / Multi-task modeling / Multitasking (Human behavior) / Neural networks / Open access publishing / Performance prediction / Prediction models / QSAR / Structure-activity relationships / Theoretical and Computational Chemistry / Toxicity / Toxicity prediction