Mol-JEPA bets on missing modalities to improve molecular prediction
Molecular pretraining often creates chemically unrealistic “views” by masking atoms or bonds, then teaches the model invariances that fail at property cliffs. Mol-JEPA takes a different route: mask whole modalities, then predict their latent representations across structures, cellular phenotypes, binding, ADMET, and quantum chemistry data. The paper reports strong results across benchmarks, suggesting biochemical context may matter more than another clever molecular augmentation.