Atomistic Representation Alignment for 3D Molecule Generation in Protein Pockets

Structure-based generative models have emerged as a powerful tool for creating novel 3D molecules within protein binding sites. However, these models often struggle to capture the fine-grained physical constraints of molecular systems. While typically trained on datasets of crystal structures and docked complexes, this spatial data provides only geometric supervision, lacking explicit information about the underlying energy landscape and atomic forces. In this work, we propose enhancing pocket-conditioned generative models by leveraging the rich internal representations of a machine-learned interatomic potential (MLIP). Because MLIPs are pre-trained on diverse quantum mechanical calculations, their latent spaces provide a highly structured, atom-aware target for our generator to learn from. By applying an equivariant projector to match the features of a denoiser layer to the MLIP, we expose the generator to a dense, physically grounded signal during training. We observe a significant improvement in the overall structural validity of the generated molecules. Evaluated on a challenging test set of out-of-distribution targets, we demonstrate that representation alignment transfers across architectures: when applied to two independent pocket-conditioned generators, it yields molecules with greater substructure overlap with native ligands and improves protein–ligand interaction fingerprint recovery in both, indicating more faithful reproduction of native binding modes. Furthermore, after a pocket-constrained relaxation in an independent computational force field, the fraction of generated molecules that are simultaneously valid, clash-free, low-strain, and well-docked increases. Together, these results indicate that aligning to an MLIP yields models whose outputs better recover native interaction patterns and converge to more physically favorable geometries.