In continuation of our earlier work (Doi: 10.1080/07391102.2019.1661876), a statistically validated and robust Bayesian model was developed on a large diverse set of HDAC8 inhibitors. The training set comprised of 676 small molecules and 293 compounds were considered as test set molecules. The findings of this analysis will help to explore some major directions regarding the HDAC8 inhibitor designing approach. Acrylamide (G1-G3, G9), N-substituted 2-phenylimidazole (G4-G8, G9, G12-G13, G16-G19), benzimidazole (G10-G11), piperidine substituted pyrrole (G13-G14) groups, alkyl/aryl amide (G15) and aryloxy carboxamide (G20) fingerprints were found to play a crucial role in HDAC8 inhibitory activity whereas -CH-N=CH- (B1, B4-B6, B14) motif, benzamide (B2-B3, B9-B13, B16-B17) groups and heptazepine (B7-B8, B15, B18-B20) group were found to influence negatively the HDAC8 inhibitory activity. The importance of such fingerprints was further validated by the HDAC8 enzyme and related inhibitor interactions at the receptor level. These results are in close agreement with those of our previous work that validate each other. Moreover, this comparative learning may enrich future endeavours regarding the designing strategy of HDAC8 inhibitors.

中文翻译：

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非活性HDAC8抑制剂的活性区分第二部分：贝叶斯分类研究以发现分子指纹。

在我们先前的工作（Doi：10.1080 / 07391102.2019.1661876）的继续中，针对大量不同的HDAC8抑制剂开发了经过统计验证的稳健贝叶斯模型。由676个小分子和293种化合物组成的训练集被视为测试集分子。该分析的结果将有助于探索有关HDAC8抑制剂设计方法的一些主要方向。丙烯酰胺（G1-G3，G9），N-取代的2-苯基咪唑（G4-G8，G9，G12-G13，G16-G19），苯并咪唑（G10-G11），哌啶取代的吡咯（G13-G14），烷基/发现芳基酰胺（G15）和芳氧基羧酰胺（G20）指纹在HDAC8抑制活性中起关键作用，而-CH-N = CH-（B1，B4-B6，B14）基序，苯甲酰胺（B2-B3，B9- B13，B16-B17）组和七氮平（B7-B8，B15，发现B18-B20）组对HDAC8抑制活性有负面影响。HDAC8酶和受体水平上相关的抑制剂相互作用进一步证实了此类指纹的重要性。这些结果与我们先前相互验证的工作非常吻合。而且，这种比较学习可以丰富关于HDAC8抑制剂设计策略的未来努力。