Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities. BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.

中文翻译：

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BitterMatch：将分子与苦味受体匹配的推荐系统

苦味是由数千种化学成分不同的化合物引发的一种厌恶暗示。苦味可能会阻止食物的消费并危及药物依从性。苦味的 G 蛋白偶联受体 TAS2Rs 在口外组织中具有物种依赖的亚型数量和不同的表达水平。TAS2R 亚型的分子识别在生理上很重要，并为配体-受体匹配提出了一个具有挑战性的案例研究。受混合推荐系统的启发，我们开发了一组新的相似性特征，并创建了 BitterMatch 算法，该算法以约 80% 的准确率和约 50% 的召回率预测配体与受体的关联。在体外测试了几种化合物的关联性，得到了 80% 的准确率和 42% 的召回率。通过包括受体特性并将实验确定的配体-受体关联与化学配体与配体的相似性相结合，实现了令人鼓舞的性能。BitterMatch 可以预测苦味药物的脱靶，识别新的配体并指导风味设计。新颖的特征捕获有关分子及其受体的信息，这些信息可以为各种化学信息学任务提供信息。随着实验数据的增加，包含邻居信息的相似性得到改善，并为分子-生物目标匹配提供了一个可概括的框架。新颖的特征捕获有关分子及其受体的信息，这些信息可以为各种化学信息学任务提供信息。随着实验数据的增加，包含邻居信息的相似性得到改善，并为分子-生物目标匹配提供了一个可概括的框架。新颖的特征捕获有关分子及其受体的信息，这些信息可以为各种化学信息学任务提供信息。随着实验数据的增加，包含邻居信息的相似性得到改善，并为分子-生物目标匹配提供了一个可概括的框架。