In a recent study published in the journal Science Advances , researchers in Sweden conducted virtual screens of over 16 million compounds using multiple receptor models developed by AlphaFold and homology modeling techniques. These models were based on different protein structures to identify trace amine-associated receptor 1 (TAAR1) agonists for the potential treatment of various neuropsychiatric conditions. They found that the AlphaFold-based screen had a higher hit rate and helped discover potent TAAR1 agonists, leading to a promising drug candidate that showed physiological effects in mice.
Study: AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine–associated receptor 1. Image Credit: Corona Borealis Studio / Shutterstock Background The advent of machine learning methods, including AlphaFold, has revolutionized protein structure prediction, achieving near-experimental accuracy and providing models for many therapeutically relevant proteins such as G-protein coupled receptors (GPCRs). This has generated significant interest in the use of AlphaFold models for drug design, as access to precise protein structures can potentially accelerate drug discovery.
However, studies comparing AlphaFold to experimentally determined GPCR structures have shown mixed results for AlphaFold's effectiveness in predicting GPCR-drug complexes. Although AlphaFold can model binding sites with high accuracy, these studies highlighted that the predicted ligand bindi.