Class-dependent machine learning based inferences

A computer-implemented method of machine learning based retrosynthesis planning, the method comprising:

• accessing a test input and N class identifiers, wherein the test input is a string specifying a structure of a chemical species corresponding to a chemical reaction product, and wherein each class identifier of the N class identifiers is a string identifying a respective class among M possible classes of chemical reactions;
• forming N test input data structures, wherein each test input data structure of the N test input data structures is formed by concatenating the test input with a different one of the N class identifiers;
• generating an inference for each of the N test input data structures using a machine learning model that is trained using examples associating example input data structures with respective example outputs, wherein each respective example input data structure is formed by concatenating an example input with a different one of the N class identifiers, each respective input data structure is a string specifying structures of chemical species corresponding to chemical reaction products, and each respective example output is a string formed by aggregating specifications of structures of two or more precursors of the chemical reaction products;
• and
• returning a class-dependent inference result for each respective test input data structure based on the inference generated for each respective test input data structure.

A computer system for performing class-dependent, machine learning based inferences, the computer system comprising:

• one or more computer processors,
• one or more computer readable storage media, and
• program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the program instructions including instructions to:
• access a test input and N class identifiers, wherein each class identifier of the N class identifiers identifies a respective class among M possible classes;
• form N test input data structures, wherein each test input data structure of the N test input data structures is formed by aggregating the test input with a different one of the N class identifiers;
• generate an inference for each of the N test input data structures using a machine learning model that is trained using examples associating example input data structures with respective example outputs, wherein each respective example input data structure is formed by aggregating an example input with a different one of the N class identifiers;
• and
• return a class-dependent inference result for each respective test input data structure based on the inference generated for each respective test input data structure.