Background: Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming.

Methods: In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search.

Results and Discussion: The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold.

Conclusion: The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.

背景：冠心病通常是由于心脏动脉壁中的胆固醇蓄积所致。他汀类药物是使用最广泛的药物，可通过抑制负责胆固醇合成的3-羟基-3-甲基戊二酰辅酶A还原酶（HMGCR）的活性位发挥作用。已经通过实验合成了一系列具有HMGCR抑制活性的阿托伐他汀类似物，这将是昂贵且费时的。

方法：在本研究中，我们采用QSAR模型和化学相似性搜索来鉴定新型HMGCR抑制剂用于心脏相关疾病。为了实现这一目标，通过将一系列结构特性与其报告为HMGCR抑制剂的阿托伐他汀类似物的生物学活性相关联，从而开发了二维QSAR模型。然后，通过使用PubChem数据库搜索对阿托伐他汀类似物进行化学相似性搜索。

结果与讨论：获得了用于HMGCR抑制的分子的三描述符电荷模型（GATS1p），连通性（SCH-7）和距离（VE1_D），其统计值为R2 = 0.67，RMSEtr = 0.33，R2 ext = 0.64和CCCext = 0.76。通过化学相似性搜索获得了109种新型化合物，并使用QSAR模型预测了这些化合物的抑制活性，该活性接近于实验观察到的阈值范围。

结论：本研究表明，QSAR模型和化学相似性搜索可结合使用，以计算机方法以较少的计算量和工作量来鉴定具有活性的新型化合物。