3D molecule generation by denoising voxel grids
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.
648924d39ab28735f19b9bee
Pedro O. Pinheiro
648924d39ab28735f19b9bef
Joshua Rackers
648924d39ab28735f19b9bf0
Joseph Kleinhenz
648924d39ab28735f19b9bf1
Michael Maser
648924d39ab28735f19b9bf2
Omar Mahmood
648924d39ab28735f19b9bf3
Andrew Martin Watkins
648924d39ab28735f19b9bf4
Stephen Ra
648924d39ab28735f19b9bf5
Vishnu Sresht
648924d39ab28735f19b9bf6
Saeed Saremi
3D molecule generation by denoising voxel grids
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.
score-based approach atomic densities regular grids denoising neural network smooth distribution real molecules neural empirical Bayes framework underdamped Langevin Markov chain Monte Carlo single step VoxMol diffusion models atom point clouds data representation noise model network architecture generative modeling algorithm