Efficient Discovery of Potent AKR1B10 Inhibitors Using Co-folding Model Boltz-2.

Although affinity prediction of protein-ligand binding remains an important challenge, cofolding models are expected to make virtual screening more effective for drug discovery and development. To verify the effectiveness of selecting a tractable number of candidates from a compound library using cofolding models, we strove to identify a novel inhibitory active compound using Boltz-2, a representative cofolding model, for aldo-keto reductase 1B10 (AKR1B10), which is highly expressed in various cancers. Consequently, of the 40 candidate compounds obtained after narrowing-down 867 candidates from a chemically diverse library based on prediction results by Boltz-2 and candidate selection with sufficient diversity, 70% (28 of 40 tested) compounds at IC50 < 10 μM were found to have inhibitory activity and to provide identification of multiple submicromolar inhibitors exhibiting novel scaffolds. Our results demonstrate that our sparse selection approach using Boltz-2 is helpful for enhancing AI-driven drug discovery. Moreover, the findings highlight its potential applicability for translating AI-generated predictions into experimentally actionable hits.