Paper ID: 2304.06779

Semi-Equivariant Conditional Normalizing Flows

Eyal Rozenberg, Daniel Freedman

We study the problem of learning conditional distributions of the form $p(G | \hat G)$, where $G$ and $\hat G$ are two 3D graphs, using continuous normalizing flows. We derive a semi-equivariance condition on the flow which ensures that conditional invariance to rigid motions holds. We demonstrate the effectiveness of the technique in the molecular setting of receptor-aware ligand generation.

Submitted: Apr 13, 2023