Development of QSAR Model Based on Monte Carlo Optimization for Predicting GABAA Receptor Binding of Newly Emerging Benzodiazepines

Abstract

The rising prevalence and appeal of designer benzodiazepines (DBZDs) pose a significant public health concern. To evaluate this threat, the biological activity/potency of DBZDs was examined through in silico studies. To gain a deeper understanding of their pharmacology, we employed the Monte Carlo optimization conformation-independent method as a tool for developing QSAR models. These models were built using optimal molecular descriptors derived from both SMILES notation and molecular graph representations. The resulting QSAR model demonstrated robustness and a high degree of predictability, proving to be very reliable. The newly discovered molecular fragments used in the computeraided design of the new compounds were believed to have caused the increase and decrease of the studied activity. Molecular docking studies were used to make the final assessment of the designed inhibitors and excellent correlation with the results of QSAR modeling was observed. This discovery paves the way for the swift prediction of binding activity for emerging benzodiazepines, offering a faster and more cost-effective alternative to traditional in vitro/in vivo analyses.