An Integrative Computational Biophysical Workflow for Molecular Glue Discovery

Targeted protein degradation (TPD), including PROTACs and molecular glues (MG), offers a path to modulate previously ‘undruggable’ targets, but the multicomponent nature of MG systems complicates binding affinity evaluation, especially without crystal structures. We present a workflow integrating AI-assisted prediction with physics-driven free energy calculations. Across 28 crystal structures, AI-assisted structure prediction models perform well in reconstructing quaternary complexes, despite the fact that all tested entries are deposited after the propose of the AI tool (i.e., not in the training set). For free energy estimation, instead of using individual scoring regimes, we propose a combined ASGBIE + Vinardo strategy that significantly outperforms either method alone. The proposed approach enables the estimation of the bioactivitity of MG from only sequence and ligand input, enabling a practical virtual screening. Further, energy decomposition enables the elucidation of protein-MG interaction mechanisms and highlights 𝜋-cation and 𝜋-methyl interactions as key contributors to MG-induced cooperativity. This integrated workflow provides mechanistic insight and practical guidance for MG design, supporting in silico discovery when experimental structures are unavailable.