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

2026-07-21

AI Summary

This work addresses a central challenge in drug discovery: designing small-molecule ligands that bind target protein pockets with high affinity and specificity. The authors propose DBMol, a novel framework that, for the first time, leverages structure prediction models such as AlphaFold-3 and Boltz-2 as unsupervised optimization signals. By integrating gradient-based affinity optimization with a flow-matching generative model, DBMol enables iterative refinement from an initial molecule while ensuring chemically valid structures. Notably, the method operates without requiring reference ligands and generates molecules exhibiting high predicted binding affinity, target specificity, and structural diversity. Experimental results demonstrate that DBMol significantly outperforms existing unconditional generative approaches in both protein pocket coverage and molecular generation quality.

Abstract

Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics. Recent breakthroughs in structure prediction, such as AlphaFold-3 and Boltz-2, enable accurate biomolecular interaction prediction and show promise as foundation models for downstream tasks, including binding affinity prediction. We propose to leverage these models and introduce DBMol, a new structure predictor-guided framework for de novo small molecule design. DBMol formulates an alternating optimization and projection process. In the optimization stage, DBMol starts from an initial molecule and uses gradient-based optimization to improve pocket-specific interactions and predicted binding affinity using a structure prediction model. In the projection stage, a flow-matching model maps the optimized molecular graph to discrete and chemically valid molecules. Experiments show that DBMol effectively optimizes the Boltz-2 affinity proxy and generates molecules with strong predicted affinity and specificity under Boltz-2 evaluation. To reduce self-confirmation bias, we further evaluate generated molecules using held-out metrics, including AF3-based evaluation. DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision. These results support the promise of structure prediction models as effective optimization signals for de novo molecular design.

Problem

• small molecule design
• protein-ligand binding
• binding affinity
• target specificity
• de novo drug design

Innovation

• structure prediction models
• de novo molecular design
• gradient-based optimization
• flow-matching
• binding affinity prediction