System of self-consistent models synergistic or antagonistic with correlation intensity index: a combined perusal using MAO-A inhibitors

Neurodegenerative disorders are considered as one of the leading causes of disability and mortality. Monoamine oxidases play a crucial role in their management. Despite many monoamine oxidase inhibitors in the market problems like hepatotoxicity, drug interactions, irreversibility and selectivity etc. present lacunas in the current therapy. Therefore, development of more effective monoamine oxidase inhibitors is required. In the present research, Monte Carlo method-based and OECD principles-guided models on 192 monoamine oxidase-A (MAO-A) inhibitors are reported using index of ideality of correlation (IIC) and correlation intensity index (CII). These two criteria are responsible for enhanced statistical characteristics values. The predictive potential of the prepared random models has been estimated through the system of self-consistence which resulted in similar statistical predictive quality of the created models using different splits of the available data. Five new molecules (M1–M5) were designed by incorporation of extracted structural attributes. The designed molecules were analysed via molecular docking study, and compound M1 showed maximum endpoint value (pIC50 = 5.5) and binding affinity (−9.5 kcal mol−1). The entire designed molecule exhibited better binding affinity and MAO-A inhibitory activity than the parent molecule. The calculated pIC50 is highly correlated with binding affinity showing correlation value of 0.9540.