Bulk and single-cell transcriptomics are widely used to characterize diseases and cellular states but remain underexplored for de novo drug discovery. Here, we present a strategy to screen and optimize compounds by matching disease transcriptomic profiles with compound-induced transcriptomic features predicted from chemical structures using a deep-learning model.