SOURCE-ANCHORED FLOW MATCHING FOR LOCAL MOLECULAR EDITING

Lead optimisation requires local edits that improve a property while preserving the parent scaffold. Property-conditioned $\text{de novo}$ generators steer samples toward a desired label without anchoring them to a given parent, so scaffold retention can only be enforced after sampling. We introduce a recipe for local property editing in the frozen latent space of a pretrained SMILES encoder. Sparse Cross-Improvement Neighbours (SCIN) mines directed source-to-target pairs whose observed property gain exceeds a margin within a latent-similarity neighbourhood, and Source-Anchored Flow Matching (SAFM) learns a velocity field conditioned on the source embedding and the property labels. Classifier-free guidance and an inference-time edit budget control the fidelity–improvement trade-off without retraining. We evaluate on ZINC250K and MOSES for QED maximisation, then transfer the same recipe to cyclic-peptide permeability on CycPeptMPDB across the PAMPA and Caco-2 assays. SAFM leads faithful editing on both modalities, at 36.3% on CycPeptMPDB, and at 9.8% on ZINC250K. Descriptor analysis of the peptide edits recovers a consistent aromatic-to-aliphatic exchange that medicinal chemistry associates with improved passive permeation. The recipe transfers across chemical modalities with no domain-specific machinery beyond a frozen encoder and modality-appropriate validity gates. $\text{Code will be released upon acceptance.}$