SE-OnionNet: A Convolution Neural Network for Protein–Ligand Binding Affinity Prediction - Wang, Shudong; Liu, Dayan; Ding, Mao; Du, Zhenzhen; Zhong, Yue; Song, Tao; Zhu, Jinfu; Zhao, Renteng (Frontiers in Genetics, 2021)
Deep learning methods, which can predict the binding affinity of a drug-target protein interaction, reduces the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two Squeeze-and-Excitation (SE) modules, to computationally predict the binding affinity of a protein-ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein-drug molecular complex. The SE module is added to the second and third convolutional layers to improve the nonlinear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein-molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and Autodock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and, thus, our model is of acceptable robustness.