Semi-Equivariant Conditional Normalizing Flows
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-Equivariant Continuous Normalizing Flows for Target-Aware Molecule Generation
We propose an algorithm for learning a conditional generative model of a molecule given a target. Specifically, given a receptor molecule that one wishes to bind to, the conditional model generates candidate ligand molec…
Graph Neural NetworkEquivariant Finite Normalizing Flows
Generative modeling seeks to uncover the underlying factors that give rise to observed data that can often be modeled as the natural symmetries that manifest themselves through invariances and equivariances to certain tr…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-c…
General ClassificationE(n) Equivariant Normalizing Flows
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E(n) graph neural networks and integrate them as a …
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the high-dimensional conditional distributions learned by a normalizing flow. We prove that a Metropolis-Hastings implementa…