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     (     ),     [[36 - .Tripathi, A. (2023) Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review, arXiv (Cornell University). Cornell University. DOI: 10.48550/ARXIV.2303.06471.],[37 - .Haque, Rezuana. (2025). Multimodal Integration with Graph Neural Networks (GNNs). DOI: 10.1007/978-3-032-04315-3_6.],[38 - .Gupta, Manish. (2024). The Evolution of Neural Networks in Artificial Intelligence for Multimodal Data Fusion. 5. 14. [ ] 2024 URL: https://iscsitr.com/articles/volume_5/issue_2/ISCSITR-IJAI_05_02_01 ( : 10.11.2025).  : .]],      ,        .     ,  ,     (GNN)  ,     , ,    ,      .        (DDI),  GNN       ,      ,          .  ,   ,    -            ,           .    ,       ,       R&D            .

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notes








1


   2860793   (RU),  C07D 239/54 (2006.01) A61K 31/505 (2006.01) A61P 35/00 (2006.01).   5--,    /  . .  .;    :    .  2025123872, . 29.08.2025 . 21.04.2026. . 2.




2


   2861491   (RU),  C07C 229/08 (2006.01), C07C 227/14 (2006.01), C07F 3/02 (2006.01), A61K 31/205 (2006.01), A61P 9/10 (2006.01), A61P 25/00 (2006.01), A61P 39/00 (2006.01).     4- ,           /  . .  .;    :    .  2025109706, . 17.04.2025 . 05.05.2026. . 13.




3


.AI in Pharmaceutical Industry: Top Use Cases, Implementation Strategies 2025. [ ] 2025 URL: https://sranalytics.io/blog/ai-in-pharmaceutical-industry/ ( : 10.11.2025).  : .




4


.Somesh Sharma. Algorithms to Treatments: AIs Impact on Modern Drug Discovery [ ] 2024 URL: https://www.aragen.com/news/algorithms-to-treatments-ais-impact-on-modern-drug-discovery/ ( : 10.11.2025).  : .




5


.Shipra Malhotra. How Exscientia Reduces Drug Discovery Time With Gen AI [ ] 2023 URL: https://www.cio.inc/how-exscientia-reduces-drug-discovery-time-gen-ai-a-23015 ( : 10.11.2025).  : .




6


.Ujwal Krishnan. Exscientias Legacy: The AI Drug Discovery Pioneer and the Recursion Merger [ ] 2023 URL: https://www.eutechfuture.com/health-tech/exscientia-ai-drug-discovery-recursion-merger/ ( : 10.11.2025).  : .




7


.The economic potential of generative AI: The next productivity frontier [ ] 2023 URL: https://www.mckinsey.com/industries/life-sciences/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier ( : 10.11.2025).  : .




8


. .    -    //   . 2006. 2. URL: https://cyberleninka.ru/article/n/v-poiskah-novyh-soedineniy-liderov-dlya-sozdaniya-lekarstv ( : 08.11.2025).




9


.Witte, H. (2006) Application of Generalized Dynamic Neural Networks to Biomedical Data, IEEE Transactions on Biomedical Engineering. Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBME.2006.881766.




10


.EU AI Act: first regulation on artificial intelligence. 2024. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligenc




11


.Medical Device Regulation. Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices [ ] 2017 URL: https://www.medical-device-regulation.eu/download-mdr/ ( : 06.11.2025).  : .




12


.     (GDPR).   [ ] 2024 URL: https://gdpr-text.com/ru/ ( : 06.11.2025).  : .




13


.Gu Xinyu, Aranganathan Akashnathan, Tiwary Pratyush (2024) Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE eLife 13:RP99702 DOI: https://doi.org/10.7554/eLife.99702.2




14


.Forker, Karly & Fleming, Matthew & Pearce, Kenneth & Vaziri, Cyrus & Bowers, Albert. (2025). Modeling is Believing? How AlphaFold2 Can Mislead Molecular Interpretation. Structural Dynamics. 12. A260-A260. DOI: 10.1063/4.0001049.




15


.Gut, Jannik & Lemmin, Thomas. (2024). Dissecting AlphaFold2s Capabilities with Limited Sequence Information. Bioinformatics Advances. 5. DOI: 10.1093/bioadv/vbae187.




16


.Wu, Tianqi & Stein, Richard & Kao, Te-Yu & Brown, Benjamin & Mchaourab, Hassane. (2025). Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions. Nature Communications. 16. DOI: 10.1038/s41467-025-62582-4.




17


.Haoran Yang, Junxia Wang, Jingxin Xie, Huiying Yang, Xianfu Wu, Molecular mechanisms and delivery strategies of celastrol targeting inflammation-associated autoimmune diseases, Journal of Ethnopharmacology, 355, (120719), (2026). DOI: https://doi.org/10.1016/j.jep.2025.120719




18


.Code for RoseTTAFold All-Atom [ ] 2025 URL: https://github.com/baker-laboratory/RoseTTAFold-All-Atom ( : 13.11.2025).  : .




19


.Hyskova, Anna & Marsalkova, Eva & Simecek, Petr. (2025). Balancing Speed and Precision in Protein Folding: A Comparison of AlphaFold2, ESMFold, and OmegaFold. DOI: 10.1101/2025.06.20.660709.




20


.Garcia, Mario & Rocklin, Gabriel & Dixit, Sugyan. (2025). Evaluating zero-shot prediction of protein design success by AlphaFold, ESMFold, and ProteinMPNN. DOI: 10.1101/2025.07.29.667290.




21


.Manfredi, Matteo & Savojardo, Castrense. (2025). Evaluation of the structural models of the human reference proteome: AlphaFold2 versus ESMFold. Current Research in Structural Biology. 9. 100167. DOI: 10.1016/j.crstbi.2025.100167.




22


.Panagiotou, Vlasios & Makris, Christos. (2025). Comparative Analysis of AlphaFold2, AlphaFold3 and ESMFold in Chimeric Antigen Receptor Prediction for Pancreatic Cancer Immunotherapy. International Journal on Artificial Intelligence Tools. DOI: 10.1142/S021821302540007X.




23


.Saxena, Ritwik & Saxena, Ritcha. (2024). Applying Graph Neural Networks in Pharmacology. DOI: 10.36227/techrxiv.170906927.71541956/v1.




24


.Conghao Wang, Gaurav Asok Kumar & Jagath C. Rajapakse. Drug discovery and mechanism prediction with explainable graph neural networks. [ ] 2025 URL: https://doi.org/10.1038/s41598-024-83090-3 ( : 10.11.2025).  : .




25


.Rajeev Chandran. Understanding Message Passing in Graph Neural Networks [ ] 2025 URL: https://medium.com/@rajeev.chandran_61731/understanding-message-passing-frameworks-in-graph-neural-networks-944d9e2a1105 ( : 10.11.2025).  : .




26


.Yanglan Gan, Wenxiao Liu, Guangwei Xu, Cairong Yan, Guobing Zou, DMFDDI: deep multimodal fusion for drugdrug interaction prediction, Briefings in Bioinformatics, Volume 24, Issue 6, November 2023, bbad397, https://doi.org/10.1093/bib/bbad397




27


.Xu, Nuo & Wang, Pinghui & Chen, Long & Tao, Jing & Zhao, Junzhou. (2019). MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions. 39683974. DOI: 10.24963/ijcai.2019/551.




28


.Wang, Hanchen & Lian, Defu & Zhang, Ying & Qin, Lu & Lin, Xuemin. (2020). GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions. DOI: 10.48550/arXiv.2005.05537.




29


.Feng, Yue-Hua & Zhang, Shao-Wu & Shi, Jian-Yu. (2020). DPDDI: a deep predictor for drug-drug interactions. BMC bioinformatics. 21. 419. DOI: 10.1186/s12859-020-03724-x.




30


.Huang, Kexin & Xiao, Cao & Hoang, Trong Nghia & Glass, Lucas & Sun, J.. (2020). CASTER: Predicting Drug Interactions with Chemical Substructure Representation. Proceedings of the AAAI Conference on Artificial Intelligence. 34. 702709. DOI: 10.1609/aaai. v34i01.5412.




31


.Lin, Xuan & Quan, Zhe & Wang, Zhi-Jie & Ma, Tengfei & Zeng, Xiangxiang. (2020). KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction. 27112717.




32


.Zhang, Qingqian & He, Changxiang & Qin, Xiaofei & Yang, Peisheng & Kong, Junyang & Mao, Yaping & Li, Die. (2024). BiTGNN: Prediction of Drug-Target Interactions Based on Bidirectional Transformer and Graph Neural Network on Heterogeneous Graph. International Journal of Biomathematics. 18. DOI: 10.1142/S1793524524500256.




33


.Wang, Yingheng & Chen, Xin & Wu, Ji. (2020). Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction. DOI:10.48550/arXiv.2010.11711.




34


.Zhao, Yaomiao & Qiao, Shaohang & Ning, Qiao & Yin, Minghao. (2025). Graph Clustering-guided Multi-view Neighborhood-enhanced Graph Contrastive Learning for Drug-Target Interaction Prediction. IEEE journal of biomedical and health informatics. PP. DOI:10.1109/JBHI.2025.3606851.




35


.Li, Dongxu & Zhao, Feifan & Yang, Yue & Cui, Ziwen & Hu, Pengwei & Hu, Lun. (2025). Multi-view Contrastive Learning for Drug-Drug Interaction Event Prediction. IEEE journal of biomedical and health informatics. PP. DOI:10.1109/JBHI.2025.3600045.




36


.Tripathi, A. (2023) Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review, arXiv (Cornell University). Cornell University. DOI: 10.48550/ARXIV.2303.06471.




37


.Haque, Rezuana. (2025). Multimodal Integration with Graph Neural Networks (GNNs). DOI: 10.1007/978-3-032-04315-3_6.




38


.Gupta, Manish. (2024). The Evolution of Neural Networks in Artificial Intelligence for Multimodal Data Fusion. 5. 14. [ ] 2024 URL: https://iscsitr.com/articles/volume_5/issue_2/ISCSITR-IJAI_05_02_01 ( : 10.11.2025).  : .




39


.Barykin A.D., Chepurnykh T.V., Osipova Z.M. Deep learning in modelling the proteinligand interaction: new pathways in drug development // Bulletin of RSMU. 2024. 1. URL: https://cyberleninka.ru/article/n/deep-learning-in-modelling-the-protein-ligand-interaction-new-pathways-in-drug-development ( : 10.11.2025).




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