CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly

Molecular conjugates, including PROTACs and peptide-drug conjugates (PDCs), derive their function from the joint behavior of multiple coupled components, yet most generative approaches design these components independently and combine them only after generation. Such staged pipelines ignore cross-component dependencies and often produce conjugates that are chemically invalid, fall outside empirical conjugate distributions, or lose function upon assembly. We introduce $\textbf{C}$o$\textbf{up}$led $\textbf{O}$bjective-Guided Discrete $\textbf{F}$lows for $\textbf{Mo}$lecular $\textbf{C}$onjugate $\textbf{A}$ssembly $(\textbf{CupOFMoCA})$, a discrete generative framework that formulates conjugate design as a constrained, coupled generation problem. CupOFMoCA restricts generative trajectories to a chemically feasible conjugate manifold and biases local transitions using target-specific activity predictors, ensuring all components remain mutually compatible throughout generation. We show that coupling constraints and objective guidance enable anticipatory design that preserves post-assembly predicted activity and produces structurally realistic conjugates across both PDC and PROTAC settings, outperforming staged baselines, including LinkerNet, DiffLinker, and DiffPROTACs for PROTACs, across assembly validity, predicted activity, and physicochemical property ranges. These results demonstrate that explicit coupling and constraint enforcement are sufficient to recover functional conjugates across conjugate classes, and provide a principled foundation for generative modeling where function emerges only at the level of the assembled system. Our anonymous code repository can be found at https://anonymous.4open.science/r/Cupofmoca-Neurips.