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

Abstract

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.