Systems and methods for using qsar data to adapt products for regions

A system for predicting a property of a product with substitute ingredients, the system comprising:

• a memory configured to store instructions;
• and
• one or more processors configured to execute the instructions to perform operations comprising:
• receiving data associated with a product, the data including an ingredient profile of the product, a molecular profile associated with the ingredient profile, and a formula profile for the product based on the ingredient profile;
• determining a set of molecular descriptors for the product based on the molecular profile;
• determining a set of material descriptors for the product based on the set of molecular descriptors;
• determining a set of formula descriptors for the product based on the set of material descriptors and the formula profile;
• and
• generating a measured set of functional properties for the product through training of a machine-learning model based on the one or more formula descriptors;
• receiving one or more substitute ingredients for the product to be evaluated with the machine-learning model;
• and
• generating a predicted set of functional properties for the product through use of the machine-learning model based on the one or more substitute ingredients.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a system for predicting a property of a product with substitute ingredients, cause the system to perform operations comprising:

• receiving data associated with a product, the data including an ingredient profile of the product, a molecular profile associated with the ingredient profile, and a formula profile for the product based on the ingredient profile;
• determining a set of molecular descriptors for the product based on the molecular profile;
• determining a set of material descriptors for the product based on the set of molecular descriptors;
• determining a set of formula descriptors for the product based on the set of material descriptors and the formula profile;
• and
• generating a measured set of functional properties for the product through training of a machine-learning model based on the one or more formula descriptors;
• receiving one or more substitute ingredients for the product to be evaluated with the machine-learning model;
• and
• generating a predicted set of functional properties for the product through use of the machine-learning model based on the one or more substitute ingredients.