Monte Carlo Method Based QSAR Modeling of Coumarin Derivates as Potent HIV‐1 Integrase Inhibitors and Molecular Docking Studies of Selected 4‐phenyl Hydroxycoumarins

Abstract

In search for new and promising coumarin compounds as HIV-1 integrase inhibitors, chemoinformatic methods like quantitative structure-activity relationships (QSAR) modeling and molecular docking have an important role since they can predict desired activity and propose molecule binding to enzyme.The aim of this study was building of QSAR models for coumarin derivatives as HIV-1 integrase inhibitors with the application of Monte Carlo method. SMILES notation was used to represent the molecular structure and for defining optimal SMILES-based descriptors. Molecular docking into rigid enzyme active site with flexible molecule was performed.Computational results indicated that this approach can satisfactorily predict the desired activity with very good statistical significance. For best built model statistical parameters were: a) 3' Processing activity: R 2 =0.9980 and Q 2 =0.9977 for training set and R 2 =0.9788 for test set and b) Integration activity: R 2 =0.9999 and Q 2 =0.9998 for training set and R 2 = 0.9213 for test set. Built QSAR models were applied to selected 4-phenyl hydroxycoumarins for calculating desired activity and for HIV-1 integrase inhibition estimation. Additionally, molecular docking study was performed to a newly identified pocket in the HIV-1 integrase enzyme structure for determination of selected 4-phenyl hydroxycoumarins binding mode.Monte Carlo method proved to be an efficient approach to build up a robust model for estimating HIV-1 integrase inhibition of coumarin compounds. Based on QSAR and molecular docking studies, 4-phenyl hydroxycoumarins can be considered as promising model compounds for developing new HIV-1 integrase inhibitors.

Monte Carlo Method‐Based QSAR Modeling of Penicillins Binding to Human Serum Proteins

“…The correlation weights (CW) of molecular features (SA k ) calculated by using CORAL software for SMILES‐based descriptors can be used for the classification of these features according to their values from three Monte Carlo optimization runs. According to their values, SA k s can be classified into three categories: SA k with the stable positive values of CW – the promoters of the increase of an endpoint value; SA k with the stable negative values of CW – the promoters of the decrease of an endpoint value; and unstable SA k , which have both positive and negative values of CW for three Monte Carlo optimization runs . This means that if the CW(SA k ) is >0 in all three runs of the Monte Carlo optimization process, then the SA k is the promoter of Ac increase; if CW(SA k ) is <0 in all three runs of the optimization, then the SA k is the promoter of Ac decrease.…”

Therapeutic potential of coumarins as antiviral agents

“…16) [68]. Likewise, several other studies also proved that 4-PC can be considered as promising model compounds for developing novel HIV-1 inhibitors [69].…”

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

“…The most important objective of any QSAR modeling is to establish a sturdy model competent to predict the idiosyncrasy of new molecules in an objective, reliable and precise manner [49,72]. Three methods are cited in the literature for evaluation of sturdiness and reliability of developed model.…”

“…Recent published papers reveal that the simplified molecular input line entry system (SMILES) is a substitute to classical QSAR methods and it can be used for the prediction of molecular structures with appropriate end point or activity [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49]. In all the QSAR models, depending on Monte Carlo optimization method, the pertinent activity is treated as random event [50][51][52][53].…”