Guide predictions with structural knowledge to improve accuracy for challenging binding modes.
Constraints allow you to incorporate experimental knowledge or structural hypotheses directly into Boltz predictions. While standard predictions to well-defined binding sites work without constraints, they become essential for covalent attachments, cryptic pockets, and ambiguous binding modes.
For well-characterized binding interactions (such as reversible inhibitors at orthosteric sites, or known protein-protein interfaces), constraints are optional.The model will find the binding site automatically.
Direct binders to non-obvious or allosteric binding sites
Molecular Glues
Enforce proximity between biomolecular chains or domains
Constraints are most valuable when you have prior structural knowledge—SAR data showing a covalent attachment, a crystal structure revealing an allosteric pocket, or biochemical evidence of a biomolecular interaction.
Bond constraints are essential for predicting structures with covalent attachments between any biomolecular components—such as covalent inhibitors (e.g., EGFR inhibitors targeting Cys797), disulfide bonds, or peptide cyclization.
Define distance restraints between atoms or residues.
Property
Description
Use Case
Known interaction sites
Inputs Required
Two entities + maximum distance (4-20 Å)
Model Behavior
Predicts a structure using the given information as a constraint
Contact constraints help when you know two regions should interact. They’re useful for enforcing proximity between any pair of biomolecules—proteins, ligands, DNA, RNA, or combinations thereof (e.g., molecular glues bridging protein chains, or ligand-DNA contacts).
Directs binder chain toward specified pocket region
Pocket constraints are critical when your target has multiple potential binding sites and you want to focus on a specific one—such as an allosteric site distinct from the orthosteric pocket. The binder chain can also be a polymer, for example, to specify the epitope that an antibody binds to on the antigen.
Constraints become even more powerful in Design Projects, where they guide virtual screening campaigns and iterative design cycles. You can apply the same constraint logic to entire libraries of compounds, ensuring all predictions respect your structural requirements.
While constraints can also be used to test multiple hypotheses, pay attention: overly restrictive constraints can force the model into unrealistic conformations.
