title: 3D molecule generation by denoising voxel grids
summary: We propose a new score-based approach to generate 3D molecules represented as
  atomic densities on regular grids. First, we train a denoising neural network
  that learns to map from a smooth distribution of noisy molecules to the
  distribution of real molecules. Then, we follow the neural empirical Bayes
  framework [Saremi and Hyvarinen, 2019] and generate molecules in two steps: (i)
  sample noisy density grids from a smooth distribution via underdamped Langevin
  Markov chain Monte Carlo, and (ii) recover the ``clean'' molecule by denoising
  the noisy grid with a single step. Our method, VoxMol, generates molecules in a
  fundamentally different way than the current state of the art (i.e., diffusion
  models applied to atom point clouds). It differs in terms of the data
  representation, the noise model, the network architecture and the generative
  modeling algorithm. VoxMol achieves comparable results to state of the art on
  unconditional 3D molecule generation while being simpler to train and faster to
  generate molecules.
paper_authors._id: 648924d39ab28735f19b9bee
paper_authors.name: Pedro O. Pinheiro
paper_authors._id: 648924d39ab28735f19b9bef
paper_authors.name: Joshua Rackers
paper_authors._id: 648924d39ab28735f19b9bf0
paper_authors.name: Joseph Kleinhenz
paper_authors._id: 648924d39ab28735f19b9bf1
paper_authors.name: Michael Maser
paper_authors._id: 648924d39ab28735f19b9bf2
paper_authors.name: Omar Mahmood
paper_authors._id: 648924d39ab28735f19b9bf3
paper_authors.name: Andrew Martin Watkins
paper_authors._id: 648924d39ab28735f19b9bf4
paper_authors.name: Stephen Ra
paper_authors._id: 648924d39ab28735f19b9bf5
paper_authors.name: Vishnu Sresht
paper_authors._id: 648924d39ab28735f19b9bf6
paper_authors.name: Saeed Saremi
paper_title: 3D molecule generation by denoising voxel grids
paper_summary: We propose a new score-based approach to generate 3D molecules represented as
  atomic densities on regular grids. First, we train a denoising neural network
  that learns to map from a smooth distribution of noisy molecules to the
  distribution of real molecules. Then, we follow the neural empirical Bayes
  framework [Saremi and Hyvarinen, 2019] and generate molecules in two steps: (i)
  sample noisy density grids from a smooth distribution via underdamped Langevin
  Markov chain Monte Carlo, and (ii) recover the ``clean'' molecule by denoising
  the noisy grid with a single step. Our method, VoxMol, generates molecules in a
  fundamentally different way than the current state of the art (i.e., diffusion
  models applied to atom point clouds). It differs in terms of the data
  representation, the noise model, the network architecture and the generative
  modeling algorithm. VoxMol achieves comparable results to state of the art on
  unconditional 3D molecule generation while being simpler to train and faster to
  generate molecules.
paper_ai_summary: VoxMol generates 3D molecules as atomic densities using a score-based approach and a denoising neural network, outperforming diffusion models on atom point clouds with simpler training and faster generation.
paper_ai_keywords: denoising neural network neural empirical Bayes underdamped Langevin Markov chain Monte Carlo score-based approach VoxMol unconditional 3D molecule generation