MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction

MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.

DeepPLA: a novel deep learning-based model for protein-ligand binding affinity prediction

The substantial cost of new drug research and development has consistently posed a huge burden and tremendous challenge for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs and identifying novel interactions between the drug molecules and the target proteins based on computational methods have gained growing attention. Here, we propose the DeepPLA, a novel deep learning-based model that combines ResNet-based 1D CNN and biLSTM, to establish an end-to-end network for protein-ligand binding affinity prediction. We first apply pre-trained embedding methods to encode the raw drug molecular SMILES strings and target protein sequences into dense vector representations. The dense vector representations separately go through ResNet-based 1D CNN modules to derive features. The extracted feature vectors are concatenated and further fed into the biLSTM network after average pooling operation, followed by the MLP module to finally predict binding affinity. We used BindingDB dataset for training and evaluating our DeepPLA model. The result shows that the DeepPLA model reaches a good performance for the protein-ligand binding affinity prediction in terms of R, RMSE, MAE, R2 and MSE with 0.89, 0.68, 0.50, 0.79 and 0.46 on the training set; and scores 0.84, 0.80, 0.60, 0.71 and 0.64 on the independent testing set, respectively. This result suggests the high accuracy of the DeepPLA prediction performance, as well as its high capability in generalization, demonstrating that the DeepPLA can be the potential upgrade to pinpoint new drug-target interactions to find better destinations for proven drugs.

Binding affinity prediction for protein–ligand complex using deep attention mechanism based on intermolecular interactions

Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA.