Other ligand-based virtual screening models are often formulated as a supervised learning problem. Modeling the association between compound structure and activity has a long history and is referred to traditionally as the quantitative structure-activity relationship (QSAR). Supervised learning formulations of ligand-based virtual screening can be viewed as modern forms of QSAR. Models, such as random forests, neural networks, and support vector machines, are trained on examples of active and inactive compounds.