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