3D molecule generation by denoising voxel grids

arXiv:2306.07473v2 Announce Type: replace
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, 19) and generate molecules in two steps: (i) sample noisy density grids from a smooth distribution via underdamped Langevin Markov chain Monte Carlo, …

abstract arxiv bayes cs.lg denoising distribution generate map molecules network neural network q-bio.qm train type voxel