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Semi-Equivariant Conditional Normalizing Flows

arXiv 2304.06779 ↗First posted 13 Apr 2023cs.LG physics.bio-ph q-bio.BMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 13 Apr 2023.

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24 of 43 references.

References · 24

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2015Variational Inference with Normalizing Flowscited by 2,170
  3. 2016Density estimation using Real NVPcited by 2,042
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  5. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  6. 2021E(n) Equivariant Graph Neural Networkscited by 965
  7. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
  8. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 594
  9. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 574
  10. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 262
  11. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 236
  12. 2020How to train your neural ODE: the world of Jacobian and kinetic regularizationcited by 228
12 more
  1. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 172
  2. 2021E(n) Equivariant Normalizing Flowscited by 165
  3. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  4. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 144
  5. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  6. 2021Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Dockingcited by 82
  7. 2020Learning Differential Equations that are Easy to Solvecited by 81
  8. 2021Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Modelscited by 73
  9. 2020STEER: Simple Temporal Regularization For Neural ODEscited by 58
  10. 2021MALI: A memory efficient and reverse accurate integrator for Neural ODEscited by 42
  11. 2017Continuous-Time Flows for Efficient Inference and Density Estimationcited by 21
  12. 2013High-Dimensional Probability Estimation with Deep Density Modelscited by 17

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