Evaluation of Ligand-Based Models on Opioids Receptors Form Street Emerged Hits

Abstract

The misuse of opioids has become a major public health crisis worldwide. Synthetic opioids, in particular, pose a significant danger due to their potency and potential for addiction. In this study, we aimed to evaluate the reliability of ligand-based models for predicting the structure of new synthetic opioids. We used the Molecular Operating Environment (MOE) software to create ligand-based models for three opioid receptors: mu, delta, and kappa. We trained the models on a dataset of known opioids, and then used them to predict the structure of new opioids based on their chemical properties. Our results showed that the ligand-based models were reliable in predicting the structure of new synthetic opioids. In fact, some of the structures predicted by the models were later identified on the street as new synthetic opioids. This demonstrates the potential of in silico modelling to aid in the identification and prediction of new synthetic opioids. In conclusion, our study highlights the utility of ligand-based models in predicting the structure of new synthetic opioids. By leveraging in silico modelling tools, we can potentially identify and predict new synthetic opioids before they emerge on the street, providing a critical tool in the fight against the opioid epidemic.