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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.
* ICLR Physics for Machine Learning (Physics4ML) Workshop 2023. arXiv
admin note: substantial text overlap with arXiv:2211.04754