The development of predictive statistical models is a common task in the field of drug design. The process of developing such models involves two main steps: building the model and then deploying the model. Traditionally such models have been deployed using Web page interfaces. This approach restricts the user to using the specified Web page, and using the model in other ways can be cumbersome. In this paper we present a flexible and generalizable approach to the deployment of predictive models, based on a Web service infrastructure using R. The infrastructure described allows one to access the functionality of these models using a variety of approaches ranging from Web pages to workflow tools. We highlight the advantages of this infrastructure by developing and subsequently deploying random forest models for two data sets.