Finding genes which are directly perturbed or targeted by drugs is of great interest and importance in drug discovery. Several network filtering methods have been created to predict the gene targets of drugs from gene expression data based on an ordinary differential equation model of the gene regulatory network (GRN). A critical step in these methods involves inferring the GRN from the expression data, which is a very challenging problem on its own. In addition, existing network filtering methods require computationally intensive parameter tuning or expression data from experiments with known genetic perturbations or both.