Plan, Guide, and Repair: Decomposed Control for Conditional Molecular Graph Diffusion

Conditional molecular graph generation must align requested properties without losing the discrete chemical structure that makes a sample usable. Existing generators typically leave molecular size, property control along the reverse trajectory, and chemical correction to a single denoising process, although these decisions require different information at different stages. We introduce Plan, Guide, and Repair (PGR), a sampling framework that controls them separately while keeping the generative backbone frozen. Plan selects a condition-dependent, training-supported molecular size before denoising. Guide learns the endpoint property implied by sampled categorical states from actual reverse trajectories and, at every reverse step, differentiates the target discrepancy on soft node and edge distributions to steer generation toward the requested condition. Repair then uses the backbone edge posterior to revise late bond choices that violate valence, aromaticity, or connectivity constraints. On joint O${}_2$/N${}_2$/CO${}_2$ generation, PGR attains 0.9991 raw validity and 0.497 average property error. On BACE, BBBP, and HIV, it reaches property accuracies of 0.9716, 0.9665, and 0.9912 with raw validities of 0.9248, 0.9549, and 0.7745. Ablations confirm the intended roles of all three components: Plan improves condition-aware size selection, Guide improves condition alignment by actively controlling reverse generation, and Repair restores raw chemical validity while preserving the alignment gain. These results support stage-specific sampling control across continuous and categorical molecular design tasks.