Abstract: Genome-wide gene expression profiles provide a means to discover the direct mediators of biologically active compounds. We have already shown that it is possible to infer a predictive model of a genetic network by overexpressing each gene of the network and measuring the resulting expression at steady state of all the genes in the network. This approach however requires the perturbation of each gene and the measurement of the perturbation magnitude. In this work we explored the possibility of inferring predictive models of large genetic networks without requiring the knowledge of which genes have been perturbed and by what amount. The network identification algorithm here described allows to infer a model of a genetic network from perturbation experiments for which the perturbed genes are not known. This model can be used to identify the target gene, or genes, of a given drug.