蛋白激酶在广泛的细胞过程中扮演着至关重要的角色，抑制激酶活性的化合物成为靶向治疗发展的主要关注点，特别是在癌症方面。因此，为了表征激酶对抑制剂治疗的反应以及下游细胞反应的努力正在越来越大规模的进行。先前使用基线细胞系描述基线特征（baseline profiling）和有限的激酶组蛋白质组分析数据的研究尝试预测小分子对细胞生存能力的影响，但这些努力未使用多剂量激酶配置文件，且具有非常有限的外部验证，预测的准确率也较低。这项研究侧重于两种大规模的基础数据类型——激酶抑制剂剖面和基因表达，来预测细胞存活筛选的结果。我们描述了我们如何结合这些数据集，检查它们与细胞生存能力的关系，并最终开发了一组计算模型，能够达到相当高的预测准确度（R2为0.78，RMSE为0.154）。使用这些模型，我们鉴定了一组激酶，其中一些激酶尚未被充分研究，它们在细胞存活预测模型中有着重要的影响。此外，我们还测试了更广泛的多组学数据集能否改善模型结果，结果发现蛋白质组激酶抑制剂剖面是单一最有信息量的数据类型。最后，我们验证了一小部分模型预测在几个三阴性和HER2阳性的乳腺癌细胞系中，证明模型在未包括在训练数据集中的化合物和细胞系中表现良好。总的来说，这个结果表明，关于激酶组的通用知识能够预测非常具体的细胞表型，有潜力被整合到靶向治疗开发的管道中。版权所有：© 2023 Berginski等。本文是按照Creative Commons Attribution License（署名许可协议）开放获取的，可在任何媒体上传播、使用、分发和复制，但必须注明原作者和来源。

Protein kinases play a vital role in a wide range of cellular processes, and compounds that inhibit kinase activity emerging as a primary focus for targeted therapy development, especially in cancer. Consequently, efforts to characterize the behavior of kinases in response to inhibitor treatment, as well as downstream cellular responses, have been performed at increasingly large scales. Previous work with smaller datasets have used baseline profiling of cell lines and limited kinome profiling data to attempt to predict small molecule effects on cell viability, but these efforts did not use multi-dose kinase profiles and achieved low accuracy with very limited external validation. This work focuses on two large-scale primary data types, kinase inhibitor profiles and gene expression, to predict the results of cell viability screening. We describe the process by which we combined these data sets, examined their properties in relation to cell viability and finally developed a set of computational models that achieve a reasonably high prediction accuracy (R2 of 0.78 and RMSE of 0.154). Using these models, we identified a set of kinases, several of which are understudied, that are strongly influential in the cell viability prediction models. In addition, we also tested to see if a wider range of multiomics data sets could improve the model results and found that proteomic kinase inhibitor profiles were the single most informative data type. Finally, we validated a small subset of the model predictions in several triple-negative and HER2 positive breast cancer cell lines demonstrating that the model performs well with compounds and cell lines that were not included in the training data set. Overall, this result demonstrates that generic knowledge of the kinome is predictive of very specific cell phenotypes, and has the potential to be integrated into targeted therapy development pipelines.Copyright: © 2023 Berginski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.