Exploring the potency of 6-bromo-3-methylquinoline analogues as prostaglandin F2α inhibitors.

Prostaglandin F2α (PGF2α) is associated with preterm labor and preterm birth. PGF2α inhibitors have thus proven to be a promising target in the development of lead compounds to prevent preterm birth. In this work, Quantitative Structural Activity Relationship (QSAR) was implemented on a dataset of 77 compounds of 6-bromo-3-methylquinoline analogues using statistical approach and random selection in the QSARINS software. The Genetic Algorithm-Multiple Linear Regression (GA-MLR) approach was used to predict the best model (  = 0.8943 and  = 0.8836). The inclusion of descriptors FNSA-2 and WV.mass resulted in a well-fitted and highly predictable model. Artificial neural network (ANN) analysis was also carried out to validate the model effectiveness. Twenty eight new molecules with better predicted biological activity (pIC50) were designed. The binding energy from the docking study of seven compounds have shown higher binding activity than P10 into prostaglandin F synthase protein (PDB ID: 2F38). The stability of protein-ligand complex was further validated by 100 ns molecular dynamics simulation and MM-PBSA binding free energy. DFT and ADME-toxicity analysis also confirmed their drug-likeness properties. Collectively, our findings highlight novel quinoline derivatives as promising lead candidates, warranting further validation through collaborative and studies.