Generative molecular modeling increasingly supports the design of ligands, proteins, binding partners, and higher-order biomolecular systems, yet most architectures still optimize these objects as partially separated tasks. This separation is problematic because pharmaceutical function often emerges from the geometry, chemistry, conformational compatibility, cooperativity, and realizability of an interface or assembly rather than from the apparent quality of either component alone. This article develops a proposed constraint-aware generative diffusion architecture for the joint co-design of proteins, ligands, binding interfaces, and molecular assemblies. The approach treats the interacting molecular system as the primary generative object and organizes it through a typed joint molecular scene, a coupled equivariant denoising process, explicit geometric, energetic, functional, and synthetic constraint channels, a constraint-arbitration layer, and a multidimensional validation boundary. The synthesis argues that no single docking, affinity, validity, novelty, confidence, or generative score can establish successful co-design. Instead, candidate systems require separable assessment of molecular structure, cross-component interaction quality, assembly organization, uncertainty, diversity, generalization, synthesizability, expression or realization feasibility, and prospective experimental behavior. The proposed architecture also distinguishes hard requirements from approximate guidance, model confidence from calibrated uncertainty, structural plausibility from energetic or mechanistic support, and computational success from pharmaceutical usefulness. Important limitations include incomplete representation of solvent, dynamics, protonation, alternative biological states, synthesis and expression constraints, negative data, and distribution shift. The contribution is therefore conceptual and methodological rather than an implemented or validated system. It is intended to clarify what molecular co-design must represent, how competing constraints should be exposed, and what evidence is required before generated complexes can inform consequential therapeutic decisions.