Abstract: A deep learning model measures functional similarities between compounds based on gene expression data for each compound. The model receives an unlabeled expression profile for a query perturbagen including transcription counts of a plurality of genes in a cell affected the query perturbagen. The model extracts an embedding of the expression profile. Using the embedding of the query perturbagen and embeddings of known perturbagens, the model determines a set of similarity scores, each indicating a likelihood that a known perturbagen has a similar effect on gene expression as the query perturbagen. The likelihood, additionally, provides a prediction that the known perturbagen and query perturbagen share pharmacological similarities. The similarity scores are ranked and, from the ranked set, at least one candidate perturbagen is determined to be pharmacologically similar to the query perturbagen.