Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting

Springer Science and Business Media LLC - Tập 11 - Trang 1-13 - 2019
Thierry Hanser1, Fabian P. Steinmetz2, Jeffrey Plante1, Friedrich Rippmann2, Mireille Krier2
1Lhasa Limited, Leeds, UK
2Merck KGaA, Darmstadt, Germany

Tóm tắt

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.

Tài liệu tham khảo

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