ApoFlowMol: Geometry-Constrained Flow Matching for 3D Molecular Generation in Flexible Protein Pockets

Deep generative models have advanced structure-based drug design (SBDD), but most methods treat protein binding pockets as rigid, overlooking ligand-associated conformational changes. Existing flexible-pocket generators address this limitation, yet residue-wise coordinate or frame updates do not inherently preserve peptide-chain geometry. We introduce ApoFlowMol, a geometry-constrained full-atom flow-matching framework that jointly generates 3D ligands and adapts apo pockets to the evolving ligand state. ApoFlowMol employs a hierarchical SE(3)-equivariant complex network to model ligand coordinates, atom and bond identities, and protein conformational changes. Protein motion is represented either by fixed-backbone local torsion updates or by chain-linked backbone-and-side-chain kinematics. Both representations preserve sequence-defined peptide links recognized among modeled pocket residues: the former leaves the backbone unchanged, whereas the latter permits backbone motion while preserving backbone bond lengths and peptide connectivity within the pocket by construction throughout sampling. ApoFlowMol generates high-affinity molecules with performance comparable to leading flexible-pocket generative models. The resulting adapted pocket conformations provide more effective conditioning than unadapted apo pockets for established fixed-pocket SBDD methods. Surprisingly, pocket conformations selected through lead-guided side-chain-flexible docking similarly enable a fixed-pocket generator to achieve performance comparable to end-to-end flexible-pocket models. Together, these results highlight the potential of geometry-constrained pocket adaptation for flexible-pocket molecular generation and for improving existing fixed-pocket SBDD methods.