Deep learning techniques rely on artificial neural networks as their building blocks. This paradigm highlights the importance of neural networks for building models to address complex systems, including protein systems. We have successfully used neural networks to construct regression models to predict binding affinity based on atomic coordinates of protein-ligand complexes. Here, we focus on a neural network model to calculate the inhibition of cyclin-dependent kinase 2 (CDK2). This enzyme is a target for the development of anticancer drugs. To build our model, we employed the atomic coordinates of a CDK2-Cyclin A2 complex and the binding affinity data available at the BindingDB. We used the program Molegro Data Modeller to construct our regression model. Our model utilizes features determined by the Molegro Virtual Docker (MVD) program and shows superior predictive performance compared with classical scoring functions. All CDK2 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme .