Computational approaches enhance the design of molecular glue degraders for undruggable proteins

Most proteins remain 'undruggable' by traditional approaches, which are unable to engage targets because of a lack of well-defined binding pockets, causing a bottleneck in drug discovery. Molecular glue degraders (MGDs) have emerged as a promising therapeutic strategy for targeting previously undruggable proteins. However, despite their potential, only a few MGDs have received FDA approval, highlighting gaps in off-target effects, drug resistance, and substrate availability. Here, we discuss recent MGD breakthroughs driven by the integration of structure-based computational approaches and AI platforms, which have accelerated MGD design with improved accuracy. Looking ahead, advances in quantum computing and AI-based generative models might open pathways to innovative treatments, targeting diseases once considered incurable.