Proteochemometric modeling (PCM) is a computational approach that can be considered an extension of quantitative structure–activity relationship (QSAR) modeling, where a single model incorporates information for a family of targets and all the associated ligands instead of modeling activity versus one target. This is especially useful for situations where bioactivity data exists for similar proteins but is scarce for the protein of interest. Here we demonstrate the application of PCM to identify allosteric modulators of metabotropic glutamate (mGlu) receptors. Given our long-running interest in modulating mGlu receptor function we compiled a matrix of compound-target bioactivity data. Some members of the mGlu family are well explored both internally and in the public domain, while there are much fewer examples of ligands for other targets such as the mGlu₇ receptor. Using a PCM approach mGlu₇ receptor hits were found. In comparison to conventional single target modeling the identified hits were more diverse, had a better confirmation rate, and provide starting points for further exploration. We conclude that the robust structure–activity relationship from well explored target family members translated to better quality hits for PCM compared to virtual screening (VS) based on a single target.

中文翻译：

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使用蛋白质化学计量学模型鉴定代谢型谷氨酸 7 受体的变构调节剂

蛋白质化学计量建模 (PCM) 是一种计算方法，可以被视为定量结构-活性关系 (QSAR) 建模的扩展，其中单个模型包含一系列靶标和所有相关配体的信息，而不是对一个靶标的活性进行建模。这对于存在类似蛋白质的生物活性数据但缺乏感兴趣的蛋白质的生物活性数据的情况特别有用。在这里，我们演示了 PCM 识别代谢型谷氨酸 (mGlu) 受体变构调节剂的应用。鉴于我们对调节 mGlu 受体功能的长期兴趣，我们编制了化合物靶标生物活性数据矩阵。_{mGlu 家族的一些成员在内部和公共领域都得到了很好的探索，而其他靶标（例如 mGlu 7}受体）的配体示例则少得多。使用 PCM 方法发现了 mGlu ₇受体命中。与传统的单目标建模相比，识别的命中更加多样化，具有更好的确认率，并为进一步探索提供了起点。我们得出的结论是，与基于单一靶标的虚拟筛选 (VS) 相比，经过充分探索的靶标家族成员的稳健构效关系转化为 PCM 的更高质量命中。