Penalized Variational Autoencoder for Molecular Design

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.