MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

GHGPR-PPIS: A graph convolutional network for identifying protein-protein interaction site using heat kernel with Generalized PageRank techniques and edge self-attention feature processing block.

Accurately pinpointing protein-protein interaction site (PPIS) on the molecular level is of utmost significance for annotating protein function and comprehending the mechanisms underpinning various diseases. While numerous computational methods for predicting PPIS have emerged, they have indeed mitigated the labor and time constraints associated with traditional experimental methods. However, the predictive accuracy of these methods has yet to reach the desired threshold. In this context, we proposed a groundbreaking graph-based computational model called GHGPR-PPIS. This innovative model leveraged a graph convolutional network using heat kernel (GraphHeat) in conjunction with Generalized PageRank techniques (GHGPR) to predict PPIS. Additionally, building upon the GHGPR framework, we devised an edge self-attention feature processing block, further augmenting the performance of the model. Experimental findings conclusively demonstrated that GHGPR-PPIS surpassed all competing state-of-the-art models when evaluated on the benchmark test set. Impressively, on two distinct independent test sets and a specific protein chain, GHGPR-PPIS consistently demonstrated superior generalization performance and practical applicability compared to the comparative model, AGAT-PPIS. Lastly, leveraging the t-SNE dimensionality reduction algorithm and clustering visualization technique, we delved into an interpretability analysis of the effectiveness of GHGPR-PPIS by meticulously comparing the outputs from different stages of the model.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.