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Semi-Equivariant Conditional Normalizing Flows (2304.06779v1)
Published 13 Apr 2023 in cs.LG, physics.bio-ph, and q-bio.BM
Abstract: 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.
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