Natural-Language-Guided Protein Generation for Ligand-Binding Design

Docking ultimately determines effective ligand binding, yet bridging global functional semantics and local physical constraints remains a central challenge for protein generation for ligand-binding design. We therefore use docking-oriented language describing docking processes, binding patterns, and local interaction constraints as generation conditions, rather than relying only on coarse-grained functional text. However, existing methods still insufficiently model docking constraints and show limited coordination among heterogeneous modalities. To address these limitations, we propose DockWizard, an encoder-router-decoder framework for multimodal protein generation. Concretely, (1) a multimodal encoder aligns function text, docking text, ligand descriptions, ligand SMILES, and ligand 3D information into shared conditional representations, improving semantic consistency across heterogeneous inputs; (2) a router module organizes heterogeneous conditions into role-specific memories by their scopes of action, preserving global functional guidance while strengthening local binding constraints; and (3) a protein decoder combines prefix guidance with full-memory injection to let different conditions act at different levels, improving controllability while preserving docking constraints during generation. We further curate a docking-language multimodal dataset with over 100,000 samples. Extensive experiments under a four-stage evaluation pipeline show that DockWizard outperforms strong baselines for this task, with average gains of 13% in docking confidence and 19% in complex stability.