This chapter describes the Gradient Descent method to predict the inhibition of protein targets. Protein systems are well-suited to study with artificial intelligence techniques, including machine learning methods. Here, we employ two variants of the Gradient Descent method: Batch Gradient Descent and Stochastic Gradient Descent. The last one is available in the Scikit-Learn library (SGDRegressor class). We can integrate Scikit-Learn methods into pipelines to build regression models addressing protein targets employed for drug discovery. In this work, we adopt a hands-on approach and show how to make a regression model to predict the inhibition of cyclin-dependent kinase 2, a protein target for anticancer drugs. We combine pair interaction data determined using the docking program AutoDock Vina and the SGDRegressor class implemented in the program SAnDReS 2.0 to create models to determine enzyme inhibition. All Jupyter Notebooks and datasets examined in this work are at GitHub: https://github.com/azevedolab/docking#readme . We made the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres .