Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

中文翻译：

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使用预测的生物活性图改善预测模型。

预测建模是早期药物开发的基石。将信息用于多个域或跨预测任务可以改善预测建模的性能。但是，聚合数据通常会导致不完整的数据矩阵，这可能会限制建模。与以前的研究一致，我们表明通过生成预测的生物活性谱并将其用作其他功能，可以提高生物学终点的预测准确性。使用保形预测（一种置信度预测器），我们提供了一个强大的框架来计算这些配置文件并评估其影响。我们报告了几种方法的结果，这些方法可在16个数据集上产生细胞毒性和生物活性的预测概况，并显示出包括来自保形预测的p值作为生物活性谱。