Integrating qHTS and QSAR Models to Identify Safe GPCR-Targeted Compounds: A Focus on hERG-Dependent Cardiotoxicity

G-protein-coupled receptors (GPCRs) are a diverse family of seven-transmembrane domain receptors that play pivotal roles in various physiological and neurological processes by mediating extracellular signals through G proteins. Notable GPCRs such as ADRB2, CHRM1, DRD2, and HTR2A are important therapeutic targets linked to conditions ranging from asthma to schizophrenia. The human ether-à-go-go-related gene (hERG), encoding the Kv11.1 potassium channel, is critical for cardiac repolarization, the inhibition of which can lead to prolonged QT intervals and an increased risk of arrhythmias. Consequently, assessing hERG-GPCR interactions is essential during drug development to enhance safety and ensure regulatory compliance. In this study, we utilized quantitative high-throughput screening (qHTS) to identify GPCR agonists and inhibitors in the Tox21 10K compound library. We applied machine-learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict selective GPCR-targeting compounds with reduced hERG liability, employing different data processing sequences. Our models trained on the Tox21 10K library screening data were subsequently validated by using the Library of Pharmacologically Active Compounds (LOPAC). Furthermore, the models were applied to virtually screen approximately 360 K diverse compounds, with the top predictions experimentally validated, revealing new GPCR modulators with minimal hERG liability. The findings provide efficient strategies for the development of lead compounds targeting GPCRs while minimizing the cardiac risks associated with hERG inhibition.