AFIMol: Adaptive Fragmentation and Fragment-Pocket Interaction Learning for 3D Molecular Generation

Generating 3D ligands conditioned on protein pockets remains a central challenge in structure-based drug design. However, fragment-based generators often rely on fixed chemical rules for molecular decomposition and lack explicit supervision for evaluating candidate fragments according to their residue-level interaction preferences. Consequently, predefined fragment boundaries may fail to capture molecule-specific structural organization, while fragment selection may not fully exploit localized interaction evidence from the protein pocket. To address these limitations, we propose AFIMol, an autoregressive framework for 3D molecular generation that integrates adaptive fragmentation with fragment-pocket interaction learning. Rather than determining fragment boundaries solely through fixed cutting rules, AFIMol learns molecule-specific boundary scores from atom-level structural responses while restricting cuts to chemically admissible bonds. At each generation step, AFIMol combines the current generation state with each candidate fragment to predict its interaction preferences over pocket residues. The interaction predictor is supervised using contact annotations from protein-ligand complexes, and the predicted residue-level interactions are aggregated into compatibility scores that adjust candidate logits during inference.This mechanism guides the generator toward fragments that better match the local pocket environment. Experiments on CrossDocked2020 show favorable synthetic accessibility and molecular diversity while maintaining competitive docking performance. Ablation studies validate the contributions of adaptive fragmentation and fragment-pocket interaction learning, while structural case studies illustrate the localized interaction patterns of the generated ligands.