ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
0/0

Penalized Variational Autoencoder for Molecular Design

preprint
revised on 12.04.2019 and posted on 12.04.2019 by Sadegh Mohammadi, Bing O'Dowd, Christian Paulitz-Erdmann, Linus Goerlitz
Variational autoencoders have emerged as one of the most common approaches for automating molecular generation. We seek to learn a cross-domain latent space capturing chemical and biological information, simultaneously. To do so, we introduce the Penalized Variational Autoencoder which directly operates on SMILES, a linear string representation of molecules, with a weight penalty term in the decoder to address the imbalance in the character distribution of SMILES strings. We find that this greatly improves upon previous variational autoencoder approaches in the quality of the latent space and the generalization ability of the latent space to new chemistry. Next, we organize the latent space according to chemical and biological properties by jointly training the Penalized Variational Autoencoder with linear units. Extensive experiments on a range of tasks, including reconstruction, validity, and transferability demonstrates that the proposed methods here substantially outperform previous SMILES and graph-based methods, as well as introduces a new way to generate molecules from a set of desired properties, without prior knowledge of a chemical structure.

History

Email Address of Submitting Author

bing.odowd@bayer.com

Institution

Bayer AG Crop Science Division

Country

Germany

ORCID For Submitting Author

None

Declaration of Conflict of Interest

None

Exports