Interpretable molecular generative models
AssigneeInternational Business Machines Corporation
InventorSeiji Takeda
Life of this patent
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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.
Claims25
A computer-implemented method for training a molecule generative model, the method comprising:
- training, by one or more processors, a molecule generative model with a dataset of molecular structures to generate an output molecular structure with a target property based on an input molecular structure with the target property;
- generating, by the one or more processors, a latent space from the dataset of molecular structures;
- and
- training, by the one or more processors, a substructure prediction model to predict one or more substructures of the output molecular structure with the target property based on the generated latent space of the input molecular structure.
A system for training a molecule generative model, the system comprising:
- one or more computer processors;
- one or more computer readable storage media;
- and
- computer program instructions to:
- train a molecule generative model with a dataset of molecular structures to generate an output molecular structure with a target property based on an input molecular structure with the target property;
- generate a latent space from the dataset of molecular structures;
- and
- train a substructure prediction model to predict one or more substructures of the output molecular structure with the target property based on the generated latent space of the input molecular structure.
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processors to perform a function, the function comprising:
- train a molecule generative model with a dataset of molecular structures to generate an output molecular structure with a target property based on an input molecular structure with the target property;
- generate a latent space from the dataset of molecular structures;
- and
- train a substructure prediction model to predict one or more substructures of the output molecular structure with the target property based on the generated latent space of the input molecular structure.
A computer-implemented method for generating a molecule with target properties and number of substructures of generated molecule associated with the target property, the method comprising:
- generating, by the one or more processors, a latent space for an input molecule with a molecule generative model;
- and
- predicting, by the one or more processors, one or more substructures of an output molecule with the one or more target properties with a substructure prediction model trained to predict one or more substructures from the latent space generated by the molecule generative model, based on the input molecule.
A system for generating a molecule with target properties and number of substructures of generated molecule associated with the target property, the system comprising:
- one or more computer processors;
- one or more computer readable storage media;
- and
- computer program instructions to:
- generate a latent space from an input molecule with a molecule generative model;
- and
- predicting, by the one or more processors, one or more substructures of an output molecule with the one or more target properties with a substructure prediction model trained to predict one or more substructures from the latent space generated by the molecule generative model, based on the input molecule.
Dependencies are inferred from the claim text.