International Business Machines Corporation
INTERPRETABLE MOLECULAR GENERATIVE MODELS

Abstract:

An approach to training a molecule generative model with interpretable a latent space to identify substructures for a generated molecule generative from the latent space generated from an input molecule with a target property may be provided. A molecule generative model may be trained with a dataset of molecular structures with associated properties and known substructures. The model may generate a latent space in which a substructure predictor model may further be trained to predict the number of substructures of a molecule with target properties from an input molecule with the target properties and identified substructures.