Protein-Drug Design

• 中文综述：药物-靶点相互作用预测的计算方法综述. 计算机工程与应用, 2023. [PDF]

• Overview: Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Briefings in Bioinformatics, 2021. [PDF]

• DTIAM: a unified framework for predicting drug-target interactions, binding affinities and drug mechanisms. Nature Communications, 2025. [PDF] [Code]

• PMMR: generalizability of drug–target binding prediction by pre-trained multi-view molecular representations. Bioinformatics, 2025. [PDF] [Code]

• GS-DTI: a graph-structure-aware framework leveraging large language models for drug–target interaction prediction. Bioinformatics, 2025. [PDF] [Code]

• MIF–DTI: a multimodal information fusion method for drug–target interaction prediction. Briefings in Bioinformatics, 2025. [PDF] [Code]

• EviDTI: Evidential deep learning-based drug-target interaction prediction. Nature Communications, 2024. [PDF] [Code]

• MUSE: A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions. Nature Communications, 2024. [PDF] [Code 1] [Code 2]

• MINDG: a drug–target interaction prediction method based on an integrated learning algorithm. Bioinformatics, 2024. [PDF] [Code]

• Accurate and transferable drug–target interaction prediction with DrugLAMP. Bioinformatics, 2024. [PDF] [Code]

• MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism. Journal of Chemical Information and Modeling, 2024. [PDF] [Code]

• DrugBAN: Interpretable bilinear attention network with domain adaptation improves drug-target prediction. Nature Machine Intelligence, 2023. [PDF] [Code 1] [Code 2]

• ConPLex: Contrastive learning in protein language space predicts interactions between drugs and protein targets. Proceedings of the National Academy of Sciences, 2023. [PDF] [Code 1] [Code 2]

• iGRLDTI: An improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network. Bioinformatics, 2023. [PDF] [Code]

• DeepMPF: Deep learning framework for predicting drug–target interactions based on multi‑modal representation with meta‑path semantic analysis. BMC Bioinformatics, 2023. [PDF] [Web Server] [Code]

• HyperAttentionDTI: Improving drug–protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics, 2022. [PDF] [Code1] [Code2]

• BridgeDPI: A novel Graph Neural Network for predicting drug–protein interactions. Bioinformatics, 2022. [PDF] [Code]

• IIFDTI: Predicting drug–target interactions through interactive and independent features based on attention mechanism. Bioinformatics, 2022. [PDF] [Code]

• KGE_NFM: A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nature Communications , 2021. [PDF] [Code]

• MolTrans: Molecular Interaction Transformer for drug-target interaction prediction. Bioinformatics, 2021. [PDF] [Code]

• DTI-Voodoo: Machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions. Bioinformatics, 2021. [PDF] [Code]

• DTI-CDF: A cascade deep forest model towards the prediction of drug-target interactions based on hybrid features. Briefings in Bioinformatics, 2021. [PDF] [Code]

• GraphDTA: Predicting drug–target binding affinity with graph neural networks. Bioinformatics, 2021. [PDF] [Code1] [Code2]

• drugVQA: Predicting drug–protein interaction using quasi-visual question answering system. Nature Machine Intelligence, 2020. [PDF] [Code]

• DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Computational Biology, 2019. [PDF] [Code]

• DTINet: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications, 2017. [PDF] [Code]