Abstract: A critical step in drug development is the optimization of the efficacy and specificity of candidate therapeutic compounds. Ideally, optimization is carried out using knowledge of the drug’s mode of action, i.e., the gene products with which a drug functionally interacts (drug targets). These drug targets may include genes that mediate the therapeutic effects of the drug, as well as genes that mediate undesirable side-effects. However, for many drug candidates the targets are unknown and difficult to identify among the thousands of genes in a typical genome. Previously, we developed an algorithm to identify drug targets in yeast using multiple perturbations to a cell and by measuring the response at steady-state (di Bernardo et al, Nature Biotechnology, in press). Here, we report a novel computational approach for rapidly identifying drug targets using time-course gene expression profiles. The approach filters expression profiles using a reverse-engineered gene-network model to distinguish the targets of compounds from the genes that exhibit only secondary responses. We tested this approach experimentally in E coli and show that it can overcome some of the experimental and computational limitations of existing chemogenetic approach for identifying a drug’s mode of action.