Prediction of Binding Affinity for ErbB Inhibitors Using Deep Neural Network Model with Morgan Fingerprints as Features

Abstract: The ErbB receptor family, including EGFR and HER2, plays a crucial role in cell growth and survival and is associated with the progression of various cancers such as breast and lung cancer. In this study, we developed a deep learning model to predict the binding affinity of ErbB inhibitors using molecular fingerprints derived from SMILES representations. The SMILES representations for each ErbB inhibitor were obtained from the ChEMBL database. We first generated Morgan fingerprints from the SMILES strings and applied AutoDock Vina docking to calculate the binding affinity values. After filtering the dataset based on binding affinity, we trained a deep neural network (DNN) model to predict binding affinity values from the molecular fingerprints. The model achieved significant performance, with a Mean Squared Error (MSE) of 0.2591, Mean Absolute Error (MAE) of 0.3658, and an R-squared value of 0.9389 on the training set. Although performance decreased slightly on the test set (R squared = 0.7731), the model still demonstrated robust generalization capabilities. These results indicate that the deep learning approach is highly effective for predicting the binding affinity of ErbB inhibitors, offering a valuable tool for virtual screening and drug discovery.