AI Co-Folding Model Boltz-2 Speeds Discovery of Novel AKR1B10 Inhibitors
- AKR1B10 is an aldo-keto reductase enzyme overexpressed in several cancer types, making it an attractive but challenging drug target.
- Researchers used Boltz-2, an AI-based protein-ligand co-folding model, to computationally screen a large, chemically diverse compound library and prioritize a small, diverse shortlist of candidates for lab testing.
- Experimental testing of that shortlist produced an unusually high hit rate, with most of the tested compounds showing real inhibitory activity against AKR1B10.
- Several confirmed hits reached submicromolar potency and carried chemical scaffolds not previously reported as AKR1B10 inhibitors.
- The results support co-folding AI models as a practical, resource-efficient way to convert computational predictions into experimentally validated drug leads, reducing reliance on brute-force screening.
• Most of a small, deliberately diverse shortlist came back with real inhibition. That hit rate isn't HTS-style luck; it's a model that has actually learned something about the pocket, not the kind of docking score I'd normally discount. • Novel scaffolds are the real prize, but I want SAR before I get excited: is potency riding on one privileged fragment, or is there a real vector to optimize? Submicromolar out of the gate on unprecedented chemotypes usually means headroom, not a dead-end analog of known reductase binders. • AKR1B10 sits close to AKR1B1 (aldose reductase) — selectivity is the whole game here; nobody needs another dirty aldo-keto reductase pan-inhibitor with off-target baggage. • This is one target, one campaign. I'd want to see it replicated on something without such a cooperative pocket before I trust co-folding over ensemble docking/FEP as a default triage step. Promising, not proven — but the direction hit-finding is heading.
Efficient Discovery of Potent AKR1B10 Inhibitors Using Co-folding Model Boltz-2 ↗