Joint Foundation Model for Synthesizable Molecular Generation and Property Prediction

Molecular foundation models deliver strong property prediction from limited labels but produce encoders that cannot propose new molecules, while latent-variable generators are trained for reconstruction rather than transfer and decode unreliably away from their data. Design needs a third ingredient neither supplies: assurance that what is proposed can actually be synthesized. The model presented here, that is a generative version of the foundation model CheMeleon, puts all three on one representation. It pairs the descriptor-supervised CheMeleon D-MPNN with an autoregressive transformer decoder, optimizing Mordred descriptor regression and SMILES reconstruction jointly at foundation scale, so that a single $2048$-dimensional embedding serves as property representation, generative latent and search domain. A rectified flow fit to cached embeddings replaces the intractable Gaussian prior and doubles as a projection operator that keeps latent search decodable. Synthesizability enters the same space rather than a post hoc filter, as a differentiable head distilled from a retrosynthesis planner and, in a second arm, as hard projection onto routed analogs. Invertibility costs $0.022$ RMSE on property prediction across 30 MoleculeACE assays. The generative capabilities, tested on the GuacaMol benchmark, are competitive to state of the art models for both unconditional and goal-oriented generation tasks. Driven by property-head gradients under Langevin dynamics, the model proposes novel, potent, synthesizable candidates on $30$ MoleculeACE assays, each with an explicit synthetic route.