Abstract
DNA-encoded library (DEL) technology, especially when combined with machine learning (ML), is a powerful method to discover novel inhibitors. DEL-ML can expand a larger chemical space and boost cost-effectiveness during hit finding. Heme oxygenase-1 (HO-1), a heme-degrading enzyme, is linked to diseases such as cancer and neurodegenerative disorders. The discovery of five series of new scaffold HO-1 hits is reported here, using a DEL-ML workflow, which emphasizes the model's uncertainty quantification and domain of applicability. This model exhibits a strong extrapolation ability, identifying new structures beyond the DEL chemical space. About 37% of predicted molecules showed a binding affinity of K (D) < 20 μM, with the strongest being 141 nM, amd 14 of those molecules displayed >100-fold selectivity for HO-1 over heme oxygenase-2 (HO-2). These molecules also showed structural novelty compared to existing HO-1 inhibitors. Docking simulations provided insights into possible selectivity rationale.