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In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method.
SAR and QSAR in Environmental Research ( IF 2.3 ) Pub Date : 2019-07-23 , DOI: 10.1080/1062936x.2019.1629998
P Kumar 1 , A Kumar 2 , J Sindhu 3
Affiliation  

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure–activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r2 = 0.8129, CCC = 0.8979 and Q2 = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.



中文翻译:

使用蒙特卡洛方法基于SMILES描述子对二酰基甘油酰基转移酶-1(DGAT1)抑制剂进行计算机设计。

糖尿病,肥胖和与代谢有关的其他疾病是全世界的健康问题。当开发出一种特定的基于酶的疗法时,这些综合征可以得到很好的治疗。二酰基甘油酰基转移酶(DGAT; EC 2.3.1.20)是一种微粒体酶,通过催化酰基辅酶A依赖性酰化作用,由1,2-二酰基甘油合成甘油三酸酯。肥胖和II型糖尿病可以通过抑制DGAT1酶来检查。定量构效关系(QSAR)建模是药物设计和开发中必不可少的技术。为了研究DGAT1抑制剂的方面,开发了基于Monte-Carlo方法的197种DGAT1抑制剂的QSAR。通过使用基于SMILES表示法的最佳描述符来导出QSAR模型。应用包括新颖的理想相关指数在内的不同统计参数来验证生成的QSAR模型。从数据集中准备了四个随机分割。统计标准分组1的验证集的r 2 = 0.8129,CCC = 0.8979和Q 2 = 0.7962是最好的;因此,已确定开发的第1部门的QSAR模型是主导模型。还确定了作为端点增加或减少的启动子的分子片段。从先导化合物DGAT011设计了十三种新的DGAT1抑制剂。

更新日期:2019-07-23
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