Discovering Potent and Diverse Agonists for the β₂‑Adrenergic Receptor via Machine Learning

Screening diverse chemical structure compounds is an essential task in modern drug discovery. It provides different opportunities to avoid patent invasion, avoid potential toxicity observed in similar compounds, and execute new potential pharmacological functions. G protein-coupled receptors (GPCRs) span an important family of membrane proteins that play a central role in signal transduction and serve as significant drug targets. A prototypical class A GPCR is the β₂-adrenergic receptor (β₂AR), which is widely targeted by agonists to treat respiratory diseases. Although various β₂AR agonists are currently available on the market, there is still an urgent demand for further optimizing drug safety, efficacy, and selectivity. Here, we combine machine learning (ML) methods with other computational methods to efficiently screen agonists against β₂AR from a large compound library, composited of 19 million molecules. Verified by cellular functional assays, we identified several extremely potent agonists showing EC₅₀ values in the range of 0.2-20 nM with new chemical structures, of which the structure is diverse from previous reported molecules. Our machine learning computational approaches provide new possibilities to design novel drug candidates for GPCR.