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DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method

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Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation

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NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction

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GIAE-DTI: Predicting Drug-Target Interactions Based on Heterogeneous Network and GIN-Based Graph Autoencoder

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KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information

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CHL-DTI: A Novel High–Low Order Information Convergence Framework for Effective Drug–Target Interaction Prediction

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Graph Convolutional Networks for Predicting Drug-Protein Interactions

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Drug‐Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

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Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding

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DTI-MHAPR: optimized drug-target interaction prediction via PCA-enhanced features and heterogeneous graph attention networks

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Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction

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GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

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Predicting Drug–Gene–Disease Associations by Tensor Decomposition for Network-Based Computational Drug Repositioning

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Drug Repurposing: Insights into Current Advances and Future Applications

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A Deep Neural Network-Based Co-Coding Method to Predict Drug-Protein Interactions by Analyzing the Feature Consistency Between Drugs and Proteins

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DTI-MPFM: A multi-perspective fusion model for predicting potential drug–target interactions

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A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug–Target Interaction Prediction

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Prediction of Drug-Target Interactions With High- Quality Negative Samples and a Network-Based Deep Learning Framework

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Drug repositioning for Parkinson’s disease: An emphasis on artificial intelligence approaches

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Identification of Potential Parkinson’s Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network

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EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction

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DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer

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Drug-target interactions prediction via graph isomorphic network and cyclic training method

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Network-Based Approaches for Drug Target Identification

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LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network

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Drug–target interactions prediction via deep collaborative filtering with multiembeddings

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Drug–Protein interaction prediction by correcting the effect of incomplete information in heterogeneous information

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Predicting Drug-Target Interactions Using Weisfeiler-Lehman Neural Network

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FPSC-DTI: drug–target interaction prediction based on feature projection fuzzy classification and super cluster fusion

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Enriching limited information on rare diseases from heterogeneous networks for drug repositioning

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AΙ-Driven Drug Repurposing: Applications and Challenges

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A heterogeneous graph neural network with automatic discovery of effective metapaths for drug–target interaction prediction

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A Knowledge-Enhanced Multi-View Framework for Drug-Target Interaction Prediction

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Predicting drug–protein interactions by preserving the graph information of multi source data

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A Network Enhancement Method to Identify Spurious Drug-Drug Interactions

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NGCN : Drug‐target interaction prediction by integrating information and feature learning from heterogeneous network

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A probabilistic knowledge graph for target identification

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A novel approach for target deconvolution from phenotype-based screening using knowledge graph

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Intelligence on the graph: Graph neural networks for mechanistic drug target discovery

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An In Silico Method for Predicting Drug Synergy Based on Multitask Learning

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Fine-grained selective similarity integration for drug–target interaction prediction

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DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

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Combination therapy synergism prediction for virus treatment using machine learning models

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CKG-IMC: An inductive matrix completion method enhanced by CKG and GNN for Alzheimer’s disease compound-protein interactions prediction

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Exploring Multi-level Mutual Information for Drug-target Interaction Prediction

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EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data

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Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network

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A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method

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Survey of Network Embedding for Drug Analysis and Prediction

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Drug-Target Interaction Prediction Based on Multi-Similarity Fusion and Sparse Dual-Graph Regularized Matrix Factorization

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A Review of Drug-related Associations Prediction Based on Artificial Intelligence Methods

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DTRE: A model for predicting drug-target interactions of endometrial cancer based on heterogeneous graph

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