Pharmacophore Optimization using Pharmacophore Mapping, QSAR, Docking, and ADMET Screening of Novel Pyrimidines Derivatives as Anticancer DNA Inhibitors

Abstract

Abstract: Cancer is a global health issue, and cancer cells' resistance to existing treatments has prompted a search for new anticancer drugs. The DNA of cancer cells is regarded as the primary target for developing new molecules. In-silico studies aid in the optimization of current pharmacophores and the development of new molecules. This study aimed to optimize the pharmacophore utilizing QSAR studies and pharmacophore mapping to generate novel chemical entities (NCEs) of pyrimidine derivatives as DNA inhibitors for cancer treatment. Furthermore, these NCEs were subjected to molecular docking and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) screening to determine their drug-likeness. This study used Schrodinger's Maestro (13.4) software for pharmacophore mapping, QSAR, molecular docking, and ADME. Toxicity was determined using the Pro Tox II online tool. Pharmacophore mapping was performed using the phase module. The QSAR model was generated using an atom-based QSAR approach. The Qik prop module was utilized for ADME prediction. Molecular docking was done in Standard precision mode. In pharmacophore mapping, we discovered that the DHHRR_1 hypothesis fitted best, with a survival score of 5.4408. The optimal atom-based QSAR model produced correlation coefficients of R2 = 0.9487 and Q2 = 0.8361. Based on QSAR research, a new set of 43 derivatives was generated. These compounds pass all ADMET requirements. In molecular docking investigations, three compounds demonstrated binding with key amino acids with a significant dock score comparable to the standard. Considering docking data and pharmacokinetic behavior of newly developed compounds, molecules NC10, NC9, and NC43 have the highest DNA binding capability.