Efficient Computational Analysis Of 3,4-dihydropyrimidine-2(1H)-one Derivatives For Design Of Potent Anti- listeriosis Agents

Abstract

A series of 3,4-dihydropyrimidine-2(1 H )-one Derivatives was Subjected for Quantitative Structure activity Relationship (QSAR) Model to Identify the important physicochemical characteristics as Anti- listeriosis . The development of predictive QSAR models depends not only on the Statistical Analysis method but also on the algorithm used for the selection of training and test sets. listeriosis is an illness Condition characterized by meningitis, encephalitis, and septicaemia caused by Listeria monocytogenes (a foodborne pathogen). The best quantitative structure activity relationship models were further validated by LOO method of cross-validation. The study of best model shows that the Thermodynamic property Like Ideal Gas Thermal Capacity and Steric Properties like Dipole-Dipole Energy, Stratching Energy contributed positively and Partition coefficient (PC) contributed negatively as anti- listeriosis. The equation obtained were validated by using the Test and Training Analysis method resulting data study that shows the Thermodynamic property Like Ideal Gas Thermal Capacity and Steric Properties Like Dipole-Dipole Energy and Stratching Energy contributed positively. The study suggest that substitution at R and R1 on 3,4-dihydropyrimidine-2(1H)-one by certain functional groups which increase the Ideal Gas Thermal Capacity, Dipole-Dipole Energy and Stretching Energy may lead to enhancement of the Anti- listeriosis activity. Attempts are made to maximize Ideal Gas Thermal Capacity, Dipole-Dipole Energy and Stratching Energy for better biological activity. The current quantitative structure activity relationship study provides important structural insights in designing of potent Anti- listeriosis .