FORGE: Reaction-Guided Generative Design of Ionizable Lipids

Ionizable lipids are essential for RNA delivery, but their discovery largely depends on synthesizing and screening libraries assembled from predefined reactants. This strategy preserves a clear synthesis route while limiting the chemical space that can be explored. *De novo* molecular generation could expand this space, yet existing whole-molecule models do not retain the reaction information needed to assemble their designs. We introduce FORGE (**F**low-matched, **O**pen-ended, **R**oute-resolved **G**eneration and **E**xploration), a generative model that uses known reaction chemistry to design ionizable lipids without restricting generation to a fixed reactant catalogue. FORGE represents each synthesis route through precursor roles, reaction-core positions, transformation order, and coarse molecular architecture, while generating the complete molecule at atom-and-bond resolution. The model does not receive component identifiers, stored precursor graphs, or fragment tokens. We evaluated FORGE on Ugi, repeated aza-Michael addition, and repeated reductive amination. Across three independent seeds, FORGE achieved exact level-one (exact-L1) transform-consistent yields of 96.4 ± 2.1%, 72.2 ± 1.6%, and 52.3 ± 3.4%, respectively. Matched whole-molecule generation followed by *post hoc* reaction filtering yielded 34.8 ± 7.1%, 19.2 ± 4.1%, and 37.0 ± 2.2%. FORGE also generated exact-L1 products with precursor identities absent from the corresponding training component catalogues across all three reactions, a capability unavailable to finite-catalogue assemblers. In an *in vivo* reporter mRNA-LNP study, three synthesized FORGE lipids demonstrated higher potency than the clinical benchmark MC3, with FORGE-3 showing approximately 13-fold higher signal. These results establish that known reaction chemistry can guide *de novo* molecular generation while preserving the information needed to verify assembly and demonstrate that generated lipids can achieve strong *in vivo* performance.