DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models
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
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- PossiblePossibly related (embedding) · 69%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
- PossiblePossibly related (embedding) · 57%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- PossiblePossibly related (embedding) · 49%AI in chemogenomics: Predicting drug-target interactions at scale - Drug Discovery News →
- LinkedLinked via arxiv author · 85%Yiming Qin →
“DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models”
- LinkedLinked via arxiv author · 85%Kai Yi →
“DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models”
- LinkedLinked via arxiv author · 85%Miruna Cretu →
“DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models”
- LinkedLinked via arxiv author · 85%Sjors H. W. Scheres →
“DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models”
- LinkedLinked via arxiv author · 85%Pietro Liò →
“DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models”