US 2025/0061978 A1 · SMALL MOLECULE GENERATION USING MACHINE LEARNING MODELS

Abstract

In various examples, systems and methods are disclosed relating to using machine learning models to generate small molecules with desired structural or physicochemical properties with high sampling efficiency. In some implementations, one or more processors receive a data structure representing a first small molecule and encode the data structure into a latent distribution of a fixed size using a machine learning model, thereby determining an encoded representation of the data structure. To generate new molecules with similar properties to the first small molecule, the processors apply noise to the encoded representation to determine a modified encoded representation. The modified encoded representation is decoded to determine a modified data structure representing a second small molecule different from the first small molecule.

Claims20

Dependent claims

A processor comprising:

one or more circuits to:
receive a data structure representing a first chemical species;
encode, using at least one machine learning model, the data structure into a latent space of a fixed size to determine an encoded representation of the data structure;
apply noise to the encoded representation to determine a modified encoded representation;
and
decode the modified representation using the at least one machine learning model to determine a modified data structure representing a second chemical species different from the first chemical species.

3 more elements

Dependencies are inferred from the claim text.

Small molecule generation using machine learning models

Life of this patent

Small molecule generation using machine learning models

Life of this patent

Abstract

Claims20