Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation

The present manuscript describes the synthesis, α-amylase inhibition, in silico studies and in-depth quantitative structure–activity relationship (QSAR) of a library of aroyl hydrazones based on benzothiazole skeleton. All the compounds of the developed library are characterized by various spectral techniques. α‐Amylase inhibitory potential of all compounds has been explored, where compound 7n exhibits remarkable α-amylase inhibition of 87.5% at 50 µg/mL. Robust QSAR models are made by using the balance of correlation method in CORAL software. The chemical structures at different concentration with optimal descriptors are represented by SMILES. A data set of 66 SMILES of 22 hydrazones at three distinct concentrations are prepared. The significance of the index of ideality of correlation (IIC) with applicability domain (AD) is also studied at depth. A QSAR model with best Rvalidation2 = 0.8587 for split 1 is considered as a leading model. The outliers and promoters of increase and decrease of endpoint are also extracted. The binding modes of the most active compound, that is, 7n in the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) are also explored by in silico molecular docking studies. Compound 7n displays high resemblance in binding mode and pose with the standard drug acarbose. Molecular dynamics simulations performed on protein–ligand complex for 100 ns, the protein gets stabilised after 20 ns and remained below 2 Å for the remaining simulation. Moreover, the deviation observed in RMSF during simulation for each amino acid residue with respect to Cα carbon atom is insignificant.
Communicated by Ramaswamy H. Sarma

本研究阐述了基于苯并噻唑骨架的芳酰腙库的合成、α-淀粉酶抑制活性测试、虚拟研究以及深入的定量构效关系（quantitative structure–activity relationship，QSAR）分析。所构建的该库中所有化合物均通过多种光谱技术完成表征。对所有化合物的α-淀粉酶抑制活性进行了考察，其中化合物7n在50 μg/mL浓度下展现出87.5%的优异α-淀粉酶抑制率。借助CORAL软件中的相关性平衡法构建了稳健的QSAR模型。不同浓度下的化合物化学结构与最优描述符均通过SMILES进行表征。构建了包含22种腙类化合物在三种不同浓度下的66条SMILES的数据集。还深入研究了相关性理想性指数（index of ideality of correlation，IIC）与适用域（applicability domain，AD）的意义。以拆分集1得到的验证集相关系数R_validation²=0.8587的QSAR模型作为最优模型。同时提取了异常值以及对响应终点具有升、降调控作用的样本。通过虚拟分子对接（in silico molecular docking）研究，考察了活性最优化合物7n在米曲霉α-淀粉酶（PDB ID：7TAA）活性位点中的结合模式。化合物7n的结合模式与结合构象与标准药物阿卡波糖高度相似。对蛋白质-配体复合物开展了100 ns的分子动力学模拟，结果显示蛋白质在20 ns后趋于稳定，并在剩余模拟时长内均保持在2 Å以下的波动幅度。此外，模拟过程中各氨基酸残基相对于Cα原子的均方根波动（root mean square fluctuation，RMSF）偏差均无统计学意义。由Ramaswamy H. Sarma转交。