# 3D molecule generation

Diffusion models for 3D molecules learn atomic distributions implicitly from data, so samples can be statistically plausible yet chemically invalid. SensDiff turns four physicochemical rules, steric non-overlap, bond length, bond angle and valence, into differentiable energies that are activated in a coarse-to-fine order with adaptive weighting during training, so the rules enter the generative dynamics rather than only correcting sampling. On the QM9 and GEOM-Drugs benchmarks it reaches 96.9% and 97.2% validity, which the authors report as better than the compared baselines.

Diffusion models for 3D molecules learn atomic distributions implicitly from data, so samples can be statistically plausible yet chemically invalid. SensDiff turns four physicochemical rules, steric non-overlap, bond length, bond angle and valence, into differentiable energies that are activated in a coarse-to-fine order with adaptive weighting during training, so the rules enter the generative dynamics rather than only correcting sampling. On the QM9 and GEOM-Drugs benchmarks it reaches 96.9% and 97.2% validity, which the authors report as better than the compared baselines.