MolLIFT: Lifting SMILES Priors into Local Frames for 3D Molecule Generation

Pocket-conditioned 3D molecule generation requires capturing both protein–ligand geometry and the chemical regularities underlying molecular structure. While 3D representations naturally describe conformations and spatial interactions, SMILES provides complementary information about molecular connectivity and chemical organization. We propose MolLIFT, a bidirectional 1D–3D Bayesian flow framework that incorporates evolving SMILES priors into pocket-conditioned 3D generation while preserving SE(3)-equivariance. MolLIFT constructs geometry-aware atom representations from local protein–ligand environments and uses atom-centered local frames to lift invariant SMILES-derived messages into explicit equivariant coordinate updates. The evolving 3D geometry is then projected back into the same local frames to provide orientation-invariant feedback for refining the SMILES belief, forming a closed bidirectional interaction throughout generation. On the CrossDocked benchmark, MolLIFT improves binding affinity, molecular quality, diversity, and success rate over the underlying property-agnostic generator. Ablation studies show that these gains stem from geometry-aware cross-modal interaction and explicit coordinate lifting rather than increased model capacity. The joint 1D–3D state further enables gradient-guided multi-objective optimization, yielding additional improvements in affinity and molecular quality. Our results establish local-frame lifting as an effective mechanism for transferring sequence-level chemical priors into equivariant 3D molecular generation.