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
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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.
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.
denoising neural network neural empirical Bayes underdamped Langevin Markov chain Monte Carlo score-based approach VoxMol unconditional 3D molecule generation