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.

中文翻译：

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插补模型相对于传统 QSAR 模型的毒性预测优势分析

最近，插补技术已被用于预测稀疏生物活性矩阵中的活性值，显示出比传统 QSAR 模型的预测性能有所提高。当预测测试化合物在特定测定中的活性时，这些模型能够将实验活性值用于辅助测定。在这项研究中，我们在三个基于分类的毒性数据集上测试了三种不同的多任务插补技术：两个小规模（每个 12 次化验）和一个大规模的 417 次化验。此外，我们详细分析了插补模型所显示的改进。我们发现，与训练化合物不同的测试化合物，以及具有大量其他测定实验值的测试化合物，显示出最大的改进。我们还研究了稀疏性对所看到的改进的影响以及所考虑的分析的相关性。我们的结果表明，与传统的单任务和多任务预测模型相比，即使是少量的附加信息也可以为插补方法提供强大的预测性能提升。