Evaluating Machine Learning Affinity Prediction Methods on Novel Lead Optimization Data: A 15‐PGDH Case Study

Recent machine learning (ML) methods for protein–ligand affinity prediction, such as Boltz‐2 and AEV‐PLIG, have been reported to approach free energy perturbation (FEP) accuracy on well‐known benchmarks. Even short of that accuracy, ML‐based potency models that fall between FEP and common docking scoring functions in accuracy and speed offer clear potential value. Here, we report a case study using recently disclosed data from four related ligand series targeting 15‐hydroxyprostaglandin dehydrogenase (15‐PGDH). We evaluate each ML method's predictive accuracy with a focus on when it could be used to influence synthetic priorities. AEV‐PLIG performed poorly across most series, and adding literature 15‐PGDH inhibitors to its training did not improve performance. Boltz‐2 performed well on series most similar to published inhibitors in its training, achieving accurate cofolding (median ligand RMSD ≤0.81 Å) and rank order. However, for a series exploring a new region of the binding pocket, Boltz‐2 sometimes generated less accurate poses and predicted all ligands in the series to be less potent than a published exemplar, despite experimental potency gains of up to 500x. These results highlight both the promise in well‐characterized chemical space and limitations in the novel regions where computational guidance is most needed.