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| 药物-蛋白质亲和力预测 | 药物-靶标相互作用预测 | 药物重定向 | 分子性质预测 | 蛋白质-蛋白质亲和力预测 |
| 药物代谢 | 药物毒理学 | 药物安全 | 抗原表位预测 | 药物-药物相互作用预测 |
| 基于配体的从头药物设计 | 基于受体的从头药物设计 | 药物知识图谱 | 药物-靶标的分子对接 | 分子逆合成设计 |
| AI分子生成 | 抗体药物发现 | 免疫治疗（含CAR-T） | 制药公司论文 | AI4Drug-Papers |
药物知识图谱
| 1. Bonner, S., Barrett, I.P., Ye, C., Swiers, R., Engkvist, O., Bender, A., et al. A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Briefings in Bioinformatics 2022. 2. Chatterjee, A., Nardi, C., Oberije, C., Lambin, P. Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape. Journal of Personalized Medicine 2021, 11. 3. Dasgupta, S., Jayagopal, A., Hong, A.L.J., Mariappan, R., Rajan, V. Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation. Jmir Medical Informatics 2021, 9. 4. Dogan, T., Atas, H., Joshi, V., Atakan, A., Rifaioglu, A.S., Nalbat, E., et al. CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations. Nucleic Acids Research 2021, 49. 5. Joshi, P., Masilamani, V., Mukherjee, A. A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network. Journal of Biomedical Informatics 2022, 132. 6. Kramer, A., Billaud, J.-N., Tugendreich, S., Shiffman, D., Jones, M., Green, J. The Coronavirus Network Explorer: mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function. Bmc Bioinformatics 2021, 22. 7. Li, Z., Zhong, Q., Yang, J., Duan, Y., Wang, W., Wu, C., et al. DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications. Bioinformatics 2022, 38, 1477-9. 8. Liu, Z., Gao, X., Li, C. Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database. Healthcare 2022, 10. 9. MacLean, F. Knowledge graphs and their applications in drug discovery. Expert Opinion on Drug Discovery 2021, 16, 1057-69. 10. Mohamed, S.K., Nounu, A., Novacek, V. Biological applications of knowledge graph embedding models. Briefings in Bioinformatics 2021, 22, 1679-93. 11. Moon, C., Jin, C., Dong, X., Abrar, S., Zheng, W., Chirkova, R.Y., et al. Original Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses. Journal of Biomedical Informatics 2021, 119. 12. Ranjan, A., Kumar, H., Kumari, D., Anand, A., Misra, R. Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph. Network modeling and analysis in health informatics and bioinformatics 2023, 12, 13-. 13. Ren, Z.-H., You, Z.-H., Yu, C.-Q., Li, L.-P., Guan, Y.-J., Guo, L.-X., et al. A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks. Briefings in Bioinformatics 2022. 14. Ren, Z.-H., Yu, C.-Q., Li, L.-P., You, Z.-H., Guan, Y.-J., Wang, X.-F., et al. BioDKG-DDI: predicting drug-drug interactions based on drug knowledge graph fusing biochemical information. Briefings in Functional Genomics 2022, 21, 216-29. 15. Rivas-Barragan, D., Domingo-Fernandez, D., Gadiya, Y., Healey, D. Ensembles of knowledge graph embedding models improve predictions for drug discovery. Briefings in Bioinformatics 2022. 16. Shin, J., Piao, Y., Bang, D., Kim, S., Jo, K. DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer. International Journal of Molecular Sciences 2022, 23. 17. Wang, M., Ma, X., Si, J., Tang, H., Wang, H., Li, T., et al. Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph. Frontiers in Genetics 2021, 11. 18. Wang, S., Du, Z., Ding, M., Rodriguez-Paton, A., Song, T. KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer's disease drug repositions. Applied Intelligence 2022, 52, 846-57. 19. Wu, Z., Zhang, X., Lin, X., editors. KGAT: Predicting Drug-Target Interaction Based on Knowledge Graph Attention Network. 18th International Conference on Intelligent Computing (ICIC); 2022 2022 Aug 07-11; Xian, PEOPLES R CHINA2022. 20. Ye, C., Swiers, R., Bonner, S., Barrett, I. A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets. IEEE/ACM transactions on computational biology and bioinformatics 2022, PP. 21. Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., et al. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nature Communications 2021, 12. 22. Yu, Y., Huang, K., Zhang, C., Glass, L.M., Sun, J., Xiao, C. SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics 2021, 37, 2988-95. 23. Zeng, X., Tu, X., Liu, Y., Fu, X., Su, Y. Toward better drug discovery with knowledge graph. Current Opinion in Structural Biology 2022, 72, 114-26. 24. Zhang, S., Yu, C., Xu, C., editors. Integrating Knowledge Graph and Bi-LSTM for Drug-Drug Interaction Predication. 18th International Conference on Intelligent Computing (ICIC); 2022 2022 Aug 07-11; Xian, PEOPLES R CHINA2022. |