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

• URL: http://arxiv.org/abs/2607.19237v1
• Date: Tue, 21 Jul 2026 16:07:07 GMT
• Title: DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
• Abstract summary: Small molecules constitute a major fraction of approved therapeutics.<n>Recent breakthroughs in structure prediction enable accurate biomolecular interaction prediction.<n>We introduce DBMol, a new structure-guided framework for de novo small molecule design.
• Score: 45.51018046127832
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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