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Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

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DataCite Commons2022-09-15 更新2024-07-29 收录
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https://tandf.figshare.com/articles/dataset/Identifications_of_good_and_bad_structural_fragments_of_hydrazone_2_5-disubstituted-1_3_4-oxadiazole_hybrids_with_correlation_intensity_index_and_consensus_modelling_using_Monte_Carlo_based_QSAR_studies_their_molecular_docking_and_ADME_anal/21080772
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The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (<i>r</i><sup>2</sup><sub>VAL</sub> = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

定量构效关系(Quantitative Structure-Activity Relationship, QSAR)与分子对接、分子动力学等计算机辅助工具(in silico)的联用,为开发2型糖尿病(T2DM)等致死性疾病的新型治疗手段提供了巨大潜力。本研究利用免费开源的CORAL软件,尝试构建基于蒙特卡洛算法的定量构效关系模型。研究选取一系列苯并噻唑偶联腙/2,5-二取代-1,3,4-噁二唑杂合物的α-淀粉酶抑制活性实验数据作为建模响应终点以开展模型构建。初始阶段,以相关强度指数(CII)作为预测性能的评价标准,共构建了8个定量构效关系模型。其中,采用CII指标、基于第6数据集拆分方式构建的模型可靠性最强,其验证集决定系数(r²<sub>VAL</sub> = 0.8739)数值最高。研究人员还从最优模型中提取了对响应终点具有显著调控作用的关键结构片段。为进一步提升预测质量并降低预测误差,本研究基于已验证的模型构建了集成共识模型。通过分子对接技术分析了所选衍生物的结合模式与构象。此外,为深入了解此类化合物在生物体内的代谢过程,研究借助免费在线工具SwissADME开展了ADME(吸收-分布-代谢-排泄)研究。
提供机构:
Taylor & Francis
创建时间:
2022-09-12
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