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Determining the quantitative structure-activity relationship (QSAR) of a related series of drugs is a central aspect of the drug design process. The machine learning program Golem from the field of inductive logic programming (ILP) applied to QSAR. ILP is the most suitable machine learning technique because it can represent the structural and relational aspects of drugs. A five-step methodology for using machine learning in drug design is presented that consists of identification of the problem, choice of a representation, induction, interpretation of results, and synthesis of new drugs.