Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design

Compositional prompts in text-to-molecule generation often specify several structural constraints at once, such as scaffolds, functional groups, substituents, and finer modifiers. Existing molecular latent diffusion models typically inject the full prompt once and apply it uniformly throughout denoising, which can cause later constraints to overwrite or distort earlier structural commitments. We propose **Chain-of-Generation (COG)**, a training-free progressive latent diffusion strategy for text-guided molecular design. Technically, CoG realizes progressive generation as multi-stage latent denoising over cumulative molecular sub-prompts: it factorizes a prompt into chemistry-informed coarse-to-fine segments, conditions each stage on the cumulative sub-prompt, and continues denoising from the previous-stage latent using a fixed partial-denoising schedule. This turns one-shot conditional generation into a sequence of stage-wise denoising steps that help retain earlier structure while introducing later constraints. Across benchmark, out-of-distribution, and ablation settings, CoG improves structural prompt fidelity over one-shot conditioning on the tested latent graph-diffusion backbones.