Figure 4.

Learning methods can be broadly divided into supervised learning, when there is data available for training and parameterisation; and unsupervised learning, when there is no such data. Unsupervised learning cannot be used for binding affinity predictions and virtual screening. Supervised learning, on the other hand, can be divided into parametric and nonparametric learning. Parametric learning assumes a predetermined functional form, as observed in linear regression, and is the method employed in classical scoring functions. Nonparametric learning, or just machine learning, does not presume a predetermined functional form, which is instead inferred from the data itself. It can yield continuous output, as in nonlinear regression, or discrete output, for classification problems such as binders/nonbinders identification.