Extensive kinase profiling data, covering more than half of the human kinome, are available nowadays and allow the construction of activity prediction models of high practical utility. Proteochemometric (PCM) approaches use compound and protein descriptors, which enables the extrapolation of bioactivity values to thus far unexplored kinases. In this study, the potential of PCM to make large‐scale predictions on the entire kinome is explored, considering the applicability on novel compounds and kinases, including clinically relevant mutants. A rigorous validation indicates high predictive power on left‐out kinases and superiority over individual kinase QSAR models for new compounds. Furthermore, external validation on clinically relevant mutant kinases reveals an excellent predictive power for mutations spread across the ATP binding site.

中文翻译：

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小分子的全基因组分析预测

如今，涵盖了超过一半人类激酶组的广泛的激酶概况分析数据可用于构建具有高度实用性的活动预测模型。蛋白质化学计量学（PCM）方法使用化合物和蛋白质描述符，这使得能够将生物活性值外推至迄今尚未开发的激酶。在这项研究中，考虑了在新型化合物和激酶（包括临床相关突变体）上的适用性，探讨了PCM对整个kinome进行大规模预测的潜力。严格的验证表明，残留激酶具有较高的预测能力，并且优于新化合物的单个激酶QSAR模型。此外，对临床相关突变激酶的外部验证揭示了跨ATP结合位点分布的突变具有出色的预测能力。