Consider two sets of entities and their members' mutual affinity values, say drug-target affinities (DTA). Drugs and targets are said to interact in their effects on DTAs if drug's effect on it depends on the target. Presence of interaction implies that assigning a drug to a target and another drug to another target does not provide the same aggregate DTA as the reversed assignment would provide. Accordingly, correctly capturing interactions enables better decision-making, for example, in allocation of limited numbers of drug doses to their best matching targets. Learning to predict DTAs is popularly done from either solely from known DTAs or together with side information on the entities, such as chemical structures of drugs and targets. In this paper, we introduce interaction directions' prediction performance estimator we call interaction concordance index (IC-index), for both fixed predictors and machine learning algorithms aimed for inferring them. IC-index complements the popularly used DTA prediction performance estimators by evaluating the ratio of correctly predicted directions of interaction effects in data. First, we show the invariance of IC-index on predictors unable to capture interactions. Secondly, we show that learning algorithm's permutation equivariance regarding drug and target identities implies its inability to capture interactions when either drug, target or both are unseen during training. In practical applications, this equivariance is remedied via incorporation of appropriate side information on drugs and targets. We make a comprehensive empirical evaluation over several biomedical interaction data sets with various state-of-the-art machine learning algorithms. The experiments demonstrate how different types of affinity strength prediction methods perform in terms of IC-index complementing existing prediction performance estimators.

翻译：考虑两组实体及其成员间的相互亲和力值，例如药物-靶点亲和力（DTA）。若药物对DTA的影响取决于靶点，则称药物与靶点在DTA效应上存在交互作用。交互作用的存在意味着：将某药物分配给特定靶点，同时将另一药物分配给另一靶点，所产生的总DTA值与反向分配方案得到的总DTA值不同。因此，准确捕捉交互作用能优化决策过程，例如在有限药物剂量分配至最佳匹配靶点的场景中。当前预测DTA的主流方法包括：仅基于已知DTA数据的学习，或结合实体辅助信息（如药物化学结构与靶点信息）的学习。本文提出一种针对交互方向预测的性能评估指标——交互一致性指数（IC-index），该指标适用于固定预测器及旨在推断交互作用的机器学习算法。IC-index通过评估数据中交互效应方向预测的正确率，对当前广泛使用的DTA预测性能评估指标形成重要补充。首先，我们证明IC-index对无法捕捉交互作用的预测器具有不变性。其次，我们揭示：若学习算法对药物与靶点标识具有置换等变性，则当训练集中未出现特定药物、靶点或两者时，该算法将无法捕捉交互作用。在实际应用中，可通过整合药物与靶点的适当辅助信息来修正这种等变性。我们在多个生物医学交互数据集上使用多种前沿机器学习算法进行了全面实证评估。实验结果表明，不同类型的亲和力强度预测方法在IC-index指标上的表现差异，这为现有预测性能评估体系提供了新的补充维度。