标题： 使用摩根指纹作为特征的深度神经网络模型预测ErbB抑制剂的结合亲和力 显示英文标题

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

摘要： ErbB受体家族，包括EGFR和HER2，在细胞生长和存活中起着关键作用，并与多种癌症（如乳腺癌和肺癌）的进展有关。 在本研究中，我们开发了一个深度学习模型，使用来自SMILES表示的分子指纹来预测ErbB抑制剂的结合亲和力。 每个ErbB抑制剂的SMILES表示从ChEMBL数据库中获得。 我们首先从SMILES字符串生成Morgan指纹，并应用AutoDock Vina对接来计算结合亲和力值。 在基于结合亲和力过滤数据集后，我们训练了一个深度神经网络（DNN）模型，从分子指纹预测结合亲和力值。 该模型在训练集上表现出显著的性能，均方误差（MSE）为0.2591，平均绝对误差（MAE）为0.3658，R平方值为0.9389。 尽管在测试集上的性能略有下降（R平方=0.7731），但该模型仍然表现出强大的泛化能力。 这些结果表明，深度学习方法对于预测ErbB抑制剂的结合亲和力非常有效，为虚拟筛选和药物发现提供了一个有价值的工具。 显示英文摘要

摘要： 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.