Optimizing Molecular Glues Using Free Energy Perturbation and Cofolding Methods

Molecular glues, a class of small molecules that induce protein-protein interactions, hold significant promise as a therapeutic modality, offering access to new biology unlocking new protein targets with enhanced specificity. Despite these advantages, their rational design and optimization remain challenging due to the dynamic nature of their interfacial binding sites and complex structure-activity relationships. Computational methods for binding affinity prediction are crucial for accelerating drug discovery, with Free Energy Perturbation (FEP) being a gold standard for accuracy at high computational cost, and co-folding models like Boltz-2 offering speed but with unproven accuracy on novel targets. This study presents the first comprehensive evaluation of FEP and Boltz-2 for predicting the binding affinity of molecular glues to protein complexes. We assessed 93 compounds across six diverse target/effector complexes, yielding 140 unique protein-compound measurements with experimentally validated ternary complex binding and degradation data. Our results demonstrate that FEP consistently outperforms Boltz-2 across all datasets in terms of correlation and RMSE, showing good absolute predictability with RMSE values within 0.3-1.25 kcal/mol as well as strong correlations. In contrast, Boltz-2 exhibited poor absolute predictability (RMSE over 3 kcal/mol in some cases) and generally poor or even negative correlations. While FEP is computationally more expensive, its accuracy makes it a valuable tool for molecular glue optimization. The poor performance of Boltz-2 suggests it is not suitable for high-throughput screening of molecular glues, highlighting the need for more accurate, high-throughput machine learning methods for pre-FEP screening. This work underscores the current capabilities and limitations of computational methods in the challenging field of molecular glue discovery.