TangentMol: Local Tangent Consistency for Few-Step 3D Molecular Generation

Diffusion and flow-based models generate 3D molecules by gradually denoising random initial structures over many steps, jointly refining atomic coordinates and chemical structure (atom types, bonds, and charges). Reducing the number of steps lowers sampling cost, but each step must then make a large update, and sample quality degrades when the model's predictions change sharply across noise levels. Consistency training mitigates this for continuous data by encouraging agreement between predictions at nearby noise levels, but it does not naturally extend to chemical structure, which changes through discrete jumps rather than along a continuous path. In this study, we introduce TangentMol, a teacher-free training objective that keeps predictions of both coordinates and chemical structure consistent across nearby noise levels. From the noisy molecule already used in standard training, a single Jacobian–vector product computes how each prediction changes along a shared coordinate–time direction. We use the coordinate tangent to form a regression target and the categorical tangents to form soft probability targets for atom, bond, and charge predictions. These terms are added to the standard denoising loss and leave the network architecture and sampler unchanged. Compared with the same-configuration VEDA-FS baseline, TangentMol reduces Fréchet ChemNet Distance from 4.15 to 0.99 on QM9 (8 steps) and from 28.60 to 13.50 on GEOM-Drugs (16 steps), while raising 4-step QM9 connected validity from 53.3% to 79.3%. At 8 steps on GEOM-Drugs, TangentMol surpasses 16-step VEDA, FlowMol2, and FlowMol3 in connected validity, molecule stability, FCD, and strain energy. In pocket-conditioned generation on CrossDocked, it raises 3D validity from 45.3% to 62.8% at 10 steps over PocketVE.