Scaffold decoration is a central task in structure-based drug design: starting from a fixed molecular core, the goal is to add peripheral fragments that improve target binding while preserving drug-like properties. Existing scaffold-completion methods often ignore the three-dimensional structure of the target protein, whereas structure-aware generative models are typically designed for unconstrained de novo generation rather than scaffold-preserving lead elaboration. We introduce MODOLO (Molecular Docking-based Lead Elaboration), an attachment-point-centric generative model for target-aware scaffold decoration. Given an incomplete scaffold, its docking pose, and the surrounding protein pocket, MODOLO represents the protein-ligand complex as a sparse heterogeneous 3D graph and uses grouped vector attention to encode local geometric interactions around each attachment point. A VAE-based Transformer then generates SMILES fragments conditioned on this pocket-aware representation, which are reattached to the scaffold to form complete molecules. On CrossDock2020, MODOLO achieves the best overall performance across affinity, pose score, scaffold similarity, synthetic accessibility, and success rate, improving success from 0.17 for the strongest non-random baseline to 0.41. The model also generalizes to an out-of-distribution PoseBusters subset, supporting its utility for structure-based lead elaboration on unseen targets.
