The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.

中文翻译：

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具有自然度量的多任务学习，用于定量结构活动关系学习

定量结构活性关系（QSAR）学习的目标是学习一个函数，在给定小分子（一种潜在药物）的结构的情况下，输出化合物的预测活性。我们采用多任务学习 (MTL) 来利用药物靶标和检测的共性。我们使用的数据集包含 ChEMBL 提供的特定化合物对药物靶点活性的精选记录。总共分析了 1091 项检测。作为基线，考虑了一种单一任务学习方法，该方法训练随机森林来单独预测每个药物靶点的药物活性。然后，我们进行了基于特征和基于实例的 MTL 来预测药物活动。我们引入了药物靶标之间进化距离的自然度量作为任务相关性的衡量标准。在 1091 个药物目标中，基于实例的 MTL 显着优于基于特征的 MTL 和基础学习器，其中 741 个药物目标。基于特征的 MTL 在 179 次中胜出，基础学习器在 171 个药物目标上表现最佳。我们得出的结论是，通过结合目标之间的进化距离，MTL QSAR 得到了改进。这些结果表明，即使特定药物靶点的可用数据很少，通过利用有关类似药物靶点的已知信息，也可以有效地进行 QSAR 学习。