Masand Vijay H, Akasapu Siddhartha, Gandhi Ajaykumar, Rastija Vesna, Patil Meghshyam K
Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India.
Corden Pharma, Colorado, USA.
Chemometr Intell Lab Syst. 2020 Nov 15;206:104172. doi: 10.1016/j.chemolab.2020.104172. Epub 2020 Oct 3.
In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R = 0.80-0.82, Q = 0.74-0.77, = 0.66-0.67). The developed QSAR models identified number of sp hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.
在本研究中，按照经合组织指南对一系列肽类严重急性呼吸综合征冠状病毒主蛋白酶（MPro）抑制剂进行了广泛的定量构效关系（QSAR）分析。该分析旨在确定决定肽类化合物MPro抑制活性的显著和潜在结构特征。QSAR分析基于一个包含62种肽类化合物的数据集，由此生成了具有统计学稳健性和高度预测性的多个模型。所有开发的模型都经过了广泛验证，并满足许多统计参数的阈值（例如，R = 0.80 - 0.82，Q = 0.74 - 0.77， = 0.66 - 0.67）。所开发的QSAR模型确定了与芳香族碳原子在七个键内的sp杂化氧原子数量、拓扑距离为3处的碳和氮原子的存在以及其他原子对的相互关系作为重要的药效团特征。因此，目前的QSAR模型在定性（描述性QSAR）和定量（预测性QSAR）方法之间取得了良好的平衡，因此对于未来肽类化合物抗SARS-CoV活性的修饰很有用。
