In an effort to reduce or eliminate the centrally associated side effects produced by opioid analgesics there has been an interest in the preparation of peripherally acting opioid receptor agonists. These compounds would have very limited or no access to the central nervous system. As a first step towards developing peripheral kappa opioid receptor (KOP) agonists, we have developed a quantitatively predictive chemical function-based pharmacophore model of selective kappa opioid receptor agonists by using the HypoGen algorithm implemented in the Catalyst software. The input for HypoGen was a training set of 26 KOP agonists exhibiting K(i) values ranging between 0.015nM and 2300nM. The best output hypothesis consists of four features: one hydrophobic (HYD), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one positive ionizable (PI) function. The predictive power of the model could be demonstrated by internal and external validation of the generated hypothesis. The resulting Catalyst pharmacophore can be used concurrently for rapid virtual screening of chemical databases to identify novel, selective KOP agonists that may be easily restricted to target tissues by synthetic modification. It is anticipated that such an approach will lead to the generation of novel selective KOP agonists that are clinically useful for the treatment of pain through peripheral mechanisms.