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]

  • DeepDTAGen: A multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation. Nature Communications, 2025. [PDF] [Code]

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

  • ScopeDTI: Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction. Nature Communications, 2025. [PDF] [Code]

  • TAPB: an interventional debiasing framework for alleviating target prior bias in drug-target interaction prediction. Nature Communications, 2025. [PDF] [Code]

  • MotifGT-DTI: Pivotal Motif-Based Graph Transformer Model Improves Drug–Target Interaction Prediction. IEEE Transactions on Neural Networks and Learning, 2025. [PDF] [Code]

  • DMFF-DTA: Dual modality feature fused neural network integrating binding site information for drug target affinity prediction. Npj Digital Medicine, 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]

  • PSF-DTI: A pseudo-label supervised graph fusion attention network for drug–target interaction prediction. Expert Systems With Applications, 2025. [PDF]

  • KNU-DTI: KNowledge United Drug-Target Interaction prediction. Computers in Biology and Medicine, 2025. [PDF] [Code]

  • ForceFM: Enhancing Protein-Ligand Predictions through Force-Guided Flow Matching. The 39th Conference on Neural Information Processing Systems, 2025. [PDF] [Code]

  • ECBind: Tokenizing Electron Cloud in Protein-Ligand Interaction Learning. arXiv, 2025. [PDF] [Code]

  • PKAG-DDI: Pairwise Knowledge-Augmented Language Model for Drug-Drug Interaction Event Text Generation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 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]

  • iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models. Bioinformatics, 2024. [PDF] [Code]

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

  • TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug–target affinities. Bioinformatics, 2024. [PDF] [Code]

  • CE-DTI: causal enhanced drug–target interaction prediction based on graph generation and multi-source information fusion. Bioinformatics, 2024. [PDF] [Code]

  • MIDTI: Drug–target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism. Bioinformatics, 2024. [PDF] [Code]

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

  • PocketDTA: An advanced multimodal architecture for enhanced prediction of drug−target affinity from 3D structural data of target binding pockets. Bioinformatics, 2024. [PDF] [Code]

  • DTI-LM: language model powered drug–target interaction prediction. 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]

  • ReduMixDTI: Prediction of Drug−Target Interaction with Feature Redundancy Reduction and Interpretable Attention Mechanism. Journal of Chemical Information and Modeling, 2024. [PDF] [Code]

  • MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction. Journal of Chemical Information and Modeling, 2024. [PDF] [Code]

  • GraphCL-DTA: A graph contrastive learning with molecular semantics for drug-target binding affinity prediction. IEEE Journal of Biomedical and Health Informatics, 2024. [PDF]

  • G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Computers in Biology and Medicine, 2024. [PDF] [Code]

  • MlanDTI: Multilevel Attention Network with Semi-supervised Domain Adaptation for Drug-Target Prediction. The Thirty-Eighth AAAI Conference on Artificial Intelligence, 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]