Multi-Anchor Latent Transduction for Out-of-Distribution Molecular Property Prediction

Predicting molecular properties under out-of-distribution (OOD) shift remains a major bottleneck in drug discovery. Transductive extrapolation is attractive because it predicts a test molecule relative to known training analogues, but existing molecular transduction methods such as Bilinear Transduction (BLT) have mainly been validated in hand-crafted descriptor spaces, where differences between descriptor vectors can be interpreted as property-relevant molecular transformations. This assumption does not reliably transfer to modern molecular encoders: learned embeddings are distributed, task-shaped representations whose nearest neighbors and difference vectors need not define useful analogies for property extrapolation. We introduce Multi-Anchor Latent Transduction (MALT), a molecular OOD transduction framework that makes retrieval geometry a learned component of the property predictor rather than relying on a fixed descriptor or frozen embedding space. MALT jointly trains the encoder, support geometry, and anchor-conditioned prediction rule under the same property objective, turning retrieved molecules into property-relevant latent supports rather than passive nearest neighbors. At inference time, MALT retrieves multiple training anchors in the learned latent space and predicts from their relations to the query. We instantiate two complementary prediction heads: a label-free latent-fusion head that uses only query and anchor embeddings, and a label-conditioned latent-delta head that extends BLT to learned molecular embeddings through multi-anchor updates. Across matched molecular OOD comparisons, MALT reduces average OOD error by 8.2–16.1% across different molecular representation modalities, with 14.2–30.1% reductions under label-range extrapolation, while maintaining competitive in-distribution performance. Code released in https://anonymous.4open.science/r/iclr_malt