DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
J Med Chem. 2021 Jun 24;64(12):8208-8220. doi: 10.1021/acs.jmedchem.1c00020. Epub 2021 Mar 26.
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.
表观遗传学靶点在药物发现研究中具有重要意义，这一点已被 8 种用于治疗癌症的已批准的表观遗传学药物以及越来越多的与表观遗传学相关的化学生物基因组数据所证明。这些数据代表了许多尚未被利用的结构-活性关系，以开发预测模型来支持药物化学研究。在此，我们报告了第一个大规模的研究，该研究涉及 26318 种化合物，这些化合物对具有表观遗传学活性的 55 个蛋白质靶标具有定量的生物学活性。我们通过系统比较在不同分子指纹上训练的机器学习模型，为小分子的表观遗传学靶标分析构建了具有高精度的预测模型。这些模型经过了全面验证，表明在表观遗传学靶标预测任务上的平均精度高达 0.952。我们的结果表明，本文报道的模型具有识别具有表观遗传学活性的小分子的巨大潜力。因此，我们的结果已被实现为一个免费的网络应用程序。
