Targeting allosteric sites offers a highly selective and effective strategy for modulating protein function. Unlike traditional drugs that bind to active sites, allosteric modulators interact with distinct regulatory regions of a protein, enabling greater specificity and fewer side effects. These modulators fine-tune protein activity rather than completely inhibiting it, making them ideal for diseases that require precise control of biological pathways.
Because allosteric sites are often less conserved than active sites, targeting them can also help overcome drug resistance, a major challenge in oncology and infectious diseases. Moreover, allosteric modulation expands the range of druggable proteins, creating new therapeutic opportunities for targets that lack well-defined active sites. Allosteric drugs can be used alone or in combination with orthosteric ligands to enhance efficacy while minimizing toxicity.
At Gain Therapeutics, we apply MagellanTM 3.0, our proprietary physics-inspired AI platform for structure-based drug discovery. Magellan 3.0 enables the automatic identification of allosteric sites and the screening of ultra-large chemical libraries containing more than 4.4 trillion compounds, to discover novel, precisely tuned therapeutics. It achieves this by integrating molecular dynamics simulations, neural networks trained on quantum mechanical data, and advanced mathematical physics techniques to model protein–ligand interactions with high accuracy. While the platform is designed for the identification of allosteric sites, it can also be used to screen any type of protein pocket, including orthosteric or active sites.
To facilitate objective comparison with other platforms, validation was performed using a representative subset of targets from the standard DUDE-Z dataset. Magellan 3.0 achieved hit-enrichment values ranging from 21 to 46 across the selected DUDE-Z targets and ranked among the top three best-performing published platforms for virtual screening and binding prediction using PDBBind 2016 dataset. These results indicate the potential of Magellan 3.0 to accelerate the discovery of selective therapeutics for challenging disease targets by linking physics-based AI-driven drug design and experimental validation
