applying a machine learning model to at least one of a first plurality of chemical compounds and a plurality of true flavor profiles corresponding to the first plurality of chemical compounds, to generate at least one of a plurality of compound projected embeddings and a plurality of positive flavor projected embeddings,

wherein the machine learning model includes a first neural network and a second neural network and is trained by, for each chemical compound in a second plurality of chemical compounds, aligning a compound projected embedding of a respective chemical compound with a positive flavor projected embedding associated with the respective chemical compound in a joint vector space, and distancing the compound projected embedding from one or more negative flavor projected embeddings associated with the respective chemical compound in the joint vector space,

wherein the compound projected embedding of the respective chemical compound is from a projection layer of the first neural network, and each of the positive flavor projected embedding and the one or more negative flavor projected embedding is from a projection layer of the second neural network;

performing a search against at least one of the plurality of compound projected embeddings or the plurality of positive flavor projected embeddings to determine one or more chemical compounds from the second plurality of chemical compounds that satisfy a request for suggested chemical compounds.