3D molecule generation by denoising voxel grids

Pedro O. Pinheiro, Joshua Rackers, joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Martin Watkins, Stephen Ra, Vishnu Sresht, Saeed Saremi

Keywords: generative model, molecule generation, drug discovery

TL;DR: New score-based approach to generate 3D molecules represented as voxel grids. The model is competitive with current state of the art while been simpler to train and faster to sample drug-like molecules.

Abstract:

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 (ie, 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.
Our experiments show that VoxMol captures the distribution of drug-like molecules better than state of the art, while being faster to generate samples.

Submission Number: 3477