DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

TL;DR - DBMol is a structure predictor-guided framework for designing chemically valid small molecules with high predicted affinity and specificity for protein pockets. It demonstrates how models such as Boltz-2 and AlphaFold-3 can provide optimization signals for de novo drug design.

• Alternates gradient-based molecular optimization with flow-matching projection into discrete, chemically valid structures.
• Uses Boltz-2 to optimize pocket interactions and predicted binding affinity without reference-ligand supervision.
• Improves pocket coverage over unconditional generation while preserving molecular diversity.
• Uses held-out metrics, including AlphaFold-3-based evaluation, to reduce self-confirmation bias.