Аннотация:This chapter illustrates the use of common regression methods and introduce performance measures for regression. The regression problem consists in estimating ligand affinity to adenosine receptor (A2A), as a function of the ligand structure. Ligand structures and their known pKi values were collected from the IupharDB, ChEMBL, and PubChem BioAssay databases. The final dataset for building regression models contains 766 compounds, out of which 384 were assigned to training, while 383 were kept aside in an external test set. The affinity value of each compound is stored in the SDF field pKi. The training set and the test set files are named respectively train.sdf and test.sdf in the folder Regression. The chapter also illustrates three regression methods: Ridge Regression (RR), the ϵ-insensitive Support Vector Machine (ϵ-SVM) and Artificial Neural Networks (ANN). Several performance measures are usually computed for regression models. The most widely used of them is the Root Mean Squared Error (RMSE).