Abstract：
Histone deacetylase 8 (HDAC8) is a zinc-dependent HDAC that catalyzes the deacetylation of nonhistone proteins. It is involved in cancer development and HDAC8 inhibitors are promising candidates as anticancer agents. However, most reported HDAC8 inhibitors contain a hydroxamic acid moiety, which often causes mutagenicity. Therefore, we used machine learning for drug screening and attempted to identify non-hydroxamic acids as HDAC8 inhibitors. In this study, we established a prediction model based on the random forest (RF) algorithm for screening HDAC8 inhibitors because it exhibited the best predictive accuracy in the training dataset, including data generated by the synthetic minority over-sampling technique (SMOTE). Using the trained RF-SMOTE model, we screened the Osaka University library for compounds and selected 50 virtual hits. However, the 50 hits in the first screening did not show HDAC8-inhibitory activity. In the second screening, using the RF-SMOTE model, which was established by retraining the dataset including 50 inactive compounds, we identified non-hydroxamic acid 12 as an HDAC8 inhibitor with an IC50 of 842 nM. Interestingly, its IC50 values for HDAC1 and HDAC3-inhibitory activity were 38 and 12 µM, respectively, showing that compound 12 has high HDAC8 selectivity. Using machine learning, we expanded the chemical space for HDAC8 inhibitors and identified non-hydroxamic acid 12 as a novel HDAC8 selective inhibitor.
摘要：
组蛋白脱乙酰酶8(HDAC8)是锌依赖性HDAC,其催化非组蛋白蛋白的脱乙酰化。它与癌症发展有关，HDAC8抑制剂是有希望的抗癌药物。然而,最多报道的HDAC8抑制剂含有异羟肟酸部分，这通常会导致诱变。因此,我们使用机器学习进行药物筛选,并尝试鉴定非异羟肟酸作为HDAC8抑制剂.在这项研究中,我们建立了一个基于随机森林（RF）算法筛选HDAC8抑制剂的预测模型，因为它在训练数据集中表现出最佳的预测精度，包括由合成少数过采样技术(SMOTE)生成的数据。使用经过训练的RF-SMOTE模型，我们筛选了大阪大学图书馆的化合物，并选择了50个虚拟命中。然而,首次筛选中的50次命中未显示HDAC8抑制活性.在第二次筛选中，使用RF-SMOTE模型，它是通过重新训练包括50种非活性化合物的数据集建立的，我们鉴定非异羟肟酸12为HDAC8抑制剂，IC50为842nM。有趣的是,其对HDAC1和HDAC3抑制活性的IC50值分别为38和12μM，分别,显示化合物12具有高HDAC8选择性。使用机器学习，我们扩展了HDAC8抑制剂的化学空间，并确定非异羟肟酸12为新型HDAC8选择性抑制剂。
