Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.

ABSTRACT: Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and an

SUBMITTER: Sakamuru S 

PROVIDER: S-EPMC9447431 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature