Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators

Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors (www.tomocomd.com) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

表观遗传药物发现是极具前景的研究领域，科学界对其关注度与日俱增，这一点从已发表的文献数量以及当前公共领域中可获取的大量结构-表观遗传活性信息中便可得到佐证。计算方法是基于结构信息分析、解析大型化合物集合活性的重要工具。本研究构建了定量结构-活性关系（Quantitative Structure-Activity Relationship，QSAR）模型，用于预测88种结构多样、性质各异的有机分子对溴结构域BRD2、BRD3及BRD4的抑制活性。研究采用RDKit软件构建了基于构象的化学结构表征方式，并完成了训练集与测试集的划分。本研究计算了双线性与三线性两类QuBiLS-MIDAS分子描述符（访问网址：www.tomocomd.com），以提取所研究化合物的几何结构特征。建模所用的基于QuBiLS-MIDAS的特征集，通过降维策略进行筛选。本研究采用结合遗传算法的多元线性回归流程构建预测模型，共构建了包含6至9个变量的回归模型，并通过多种内部与外部验证方法对模型开展评估。此外，本研究对各模型的离群化合物及模型适用域进行了分析。结果显示，针对BRD2与BRD3的8变量模型以及针对BRD4的9变量模型，在本次研究采用的评价标准下综合性能最优。本研究结果表明，所提出的模型可作为表观遗传药物发现过程中，研究候选药物对目标溴结构域抑制活性的有效工具。