The growing interest in epigenetic probes and drug discovery, as revealed by several epigenetic drugs in clinical use or in the lineup of the drug development pipeline, is boosting the generation of screening data. In order to maximize the use of structure–activity relationships there is a clear need to develop robust and accurate models to understand the underlying structure–activity relationship. Similarly, accurate models should be able to guide the rational screening of compound libraries. Herein we introduce a novel approach for epigenetic quantitative structure–activity relationship (QSAR) modelling using conformal prediction. As a case study, we discuss the development of models for 11 sets of inhibitors of histone deacetylases (HDACs), which are one of the major epigenetic target families that have been screened. It was found that all derived models, for every HDAC endpoint and all three significance levels, are valid with respect to predictions for the external test sets as well as the internal validation of the corresponding training sets. Furthermore, the efficiencies for the predictions are above 80% for most data sets and above 90% for four data sets at different significant levels. The findings of this work encourage prospective applications of conformal prediction for other epigenetic target data sets.

正如表观遗传药物在临床使用中或在药物开发管道的阵容中所揭示的那样，对表观遗传探针和药物发现的兴趣日益增长，这正在促进筛选数据的产生。为了最大程度地利用结构－活动关系，显然需要开发健壮和准确的模型来理解基础的结构－活动关系。同样，准确的模型应该能够指导化合物库的合理筛选。在这里，我们介绍一种使用保形预测进行表观遗传定量结构-活性关系（QSAR）建模的新方法。作为案例研究，我们讨论了11组组蛋白脱乙酰基酶（HDAC）抑制剂的模型开发，这是已筛选的主要表观遗传学靶标家族之一。结果发现，对于每个HDAC端点和所有三个显着性水平，所有派生模型对于外部测试集的预测以及相应训练集的内部验证都是有效的。此外，对于大多数数据集，预测的效率在80％以上，对于四个数据集，在不同的显着水平上，预测的效率在90％以上。这项工作的发现鼓励对其他表观遗传目标数据集进行保形预测的预期应用。