Improving Chemical Autoencoder Latent Space and Molecular De Novo Generation Diversity with Heteroencoders.

ABSTRACT: Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here, it is shown that the choice of chemical representation, such as strings from the simplified molecular-input line-entry system (SMILES), has a large influence on the properties of the latent space. It is further explored to what extent translating between different chemical representations influences the latent space similarity to the SMILES strings or circular fingerprints. By employing SMILES enumeration for either the encoder or decoder, it is found that the decoder has the largest influence

SUBMITTER: Bjerrum EJ 

PROVIDER: S-EPMC6316879 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature