In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart. Moreover, by training a probabilistic model on a large dataset of molecular conformations, we demonstrate how our model can be used to generate diverse sets of energetically favorable conformations for a given molecule. Finally, we show that the continuous representation allows us to utilize optimization methods to find molecules that have conformations with favourable spatial properties.
@article{arxiv.2101.01618,
title = {Auto-Encoding Molecular Conformations},
author = {Robin Winter and Frank Noé and Djork-Arné Clevert},
journal= {arXiv preprint arXiv:2101.01618},
year = {2021}
}
Comments
6 pages, 2 figures, presented at Machine Learning for Molecules Workshop at NeurIPS 2020