Caspase-3 Inhibition Prediction of Pyrrolo[3,4-c] Quinoline-1,3-Diones Derivatives Using Computational Tools

对接(动物) 生物信息学 线性回归 数量结构-活动关系 化学 变构调节 适用范围 计算生物学 喹啉 立体化学 机器学习 计算机科学 生物化学 生物 基因 护理部 医学 有机化学
作者
ana P,ey
出处
期刊:Indian Journal of Pharmaceutical Sciences [Medknow]
卷期号:83 (3) 被引量:1
标识
DOI:10.36468/pharmaceutical-sciences.799
摘要

In the present work, two dimensional quantitative structure activity relationship, molecular docking and absorption, distribution, metabolism, excretion and toxicity analyses were performed to pyrrolo[3,4-c] quinoline-1,3-diones derivatives, previously reported as caspase-3 inhibitors. A total of one hundred fifteen compounds were used to build linear multiple linear regression (multiple linear regression) and non-linear (artificial neural networks) quantitative structure activity relationship models, using genetic algorithm as a feature selection method. Both models were thoroughly validated following Organization for economic cooperation and development principles by internal and external validation as well as the domain of application (antiphase domain). Both Genetic algorithm-multiple linear regression (Rtrain=0.88, Rtest=0.94, mapetest=5.3 and rmsetest=0.41) and Genetic algorithm-artificial neural network (Rtrain=0.9, Rtest=0.93, mapetest=4.5 and rmsetest=0.4) models are statistically robust with high external predictive ability. Molecular docking simulations were performed on selected inhibitors revealed that binding energy values are in accordance with inhibitory activity values against caspase-3, which is modulated by hydrogen bondings, Pi stacking and hydrophobic interactions. The docking studies suggest that the inhibitors bind with an allosteric site of the enzyme formed by ARG207B, SER251B, PHE250 and PHE256 of the B chain. Besides, in silico, absorption, distribution, metabolism, excretion and toxicity profiles of selected inhibitors were checked to evaluate the key pharmacokinetic, physiochemical and druglikeness features.
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