In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.

中文翻译：

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通过不同 (Q)SAR 方法和数据源的协同组合，避免药物设计中的 hERG 责任：工业环境中的案例研究。

在本文中，我们探讨了结合不同的计算机预测方法和数据源对所得系统的预测性能的影响。我们使用 hERG 离子通道靶标的抑制作为本研究的终点，因为它构成了药物开发中的关键安全问题和潜在的磨损原因。我们将证明，组合数据源可以提高训练集与目标化学空间的相关性，从而提高性能。同样，我们将证明，在考虑到合并系统预测的置信度时，将多个统计模型与专家系统组合在一起可以产生积极的协同效应。分析的最佳组合显示出良好的 hERG 预测能力。最后，这项工作证明了 SOHN 方法在使用适当的药效基团描述符时，在基于受体的终点（例如 hERG 抑制）的背景下构建模型的适用性。