Molecular Hypergraph Grammar with Its Application to Molecular Optimization
Authors
Authors
- Hiroshi Kajino
Authors
- Hiroshi Kajino
Published on
06/15/2019
Molecular optimization aims to discover novel molecules with desirable properties, and its two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO), where VAE converts a molecule into/from its latent continuous vector, and BO optimizes a latent continuous vector (and its corresponding molecule) within a limited number of property evaluations. While the most recent work, for the first time, achieved 100% validity, its architecture is rather complex due to auxiliary neural networks other than VAE, making it difficult to train. This paper presents a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to achieve 100% validity. Our idea is to develop a graph grammar encoding the hard chemical constraints, called molecular hypergraph grammar (MHG), which guides VAE to always generate valid molecules. We also present an algorithm to construct MHG from a set of molecules.
Please cite our work using the BibTeX below.
@InProceedings{pmlr-v97-kajino19a,
title = {Molecular Hypergraph Grammar with Its Application to Molecular Optimization},
author = {Kajino, Hiroshi},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {3183--3191},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/kajino19a/kajino19a.pdf},
url = {https://proceedings.mlr.press/v97/kajino19a.html}
}