Quantitative structure‐activity relationship models (QSAR models) predict the physical properties or biological effects based on physicochemical properties or molecular descriptors of chemical structures. Our work focuses on the construction of optimal linear and nonlinear weighted mixes of individual QSAR models to more accurately predict their performance. How the splitting of the application domain by a nonlinear gating network in a “mixture of experts” model structure is suitable for the determination of the optimal domain‐specific QSAR model and how the optimal QSAR model for certain chemical groups can be determined is highlighted. The input of the gating network is arbitrarily formed by the various molecular structure descriptors and/or even the prediction of the individual QSAR models. The applicability of the method is demonstrated on the pK a values of the OASIS database (1912 chemicals) by the combination of four acidic pK a predictions of the OECD QSAR Toolbox. According to the results, the prediction performance was enhanced by more than 15% (root‐mean‐square error [RMSE] value) compared with the predictions of the best individual QSAR model.

中文翻译：

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QSAR 模型的混合：学习 pK a 预测器的应用领域

定量构效关系模型（QSAR 模型）基于物理化学特性或化学结构的分子描述符来预测物理特性或生物效应。我们的工作侧重于构建单个 QSAR 模型的最佳线性和非线性加权混合，以更准确地预测其性能。强调了“专家混合”模型结构中非线性门控网络对应用领域的拆分如何适用于确定最佳特定领域的 QSAR 模型，以及如何确定某些化学基团的最佳 QSAR 模型。门控网络的输入由各种分子结构描述符和/或单个 QSAR 模型的预测任意形成。该方法的适用性通过 OECD QSAR 工具箱的四个酸性 pK a 预测的组合在 OASIS 数据库（1912 化学品）的 pK a 值上得到证明。根据结果​​，与最佳个体 QSAR 模型的预测相比，预测性能提高了 15% 以上（均方根误差 [RMSE] 值）。