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JAK3激酶小分子抑制剂虚拟筛选设计技术的相关研究进展
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Abstract:
JAK抑制剂因能够抑制JAK/STAT信号通路从而治疗炎症和自身免疫疾病，逐渐成为药物研究领域的热点，但面对庞大的潜在药物分子如何有效地筛选从而得到有效的药物分子仍然面临着挑战。因此通过计算机辅助处理分子信息，并结合分子对接、药代动力学、分子动力学、机器学习等相关技术进行药物分子的虚拟筛选从而为接下来的药物合成，生物测试等提供重要的研究思路，本文主要综述使用计算机辅助药物设计技术在JAK小分子抑制剂研究中的相关进展，介绍其相关的设计思路，并对计算机辅助药物设计的发展前景做出展望。
JAK inhibitors, which can suppress the JAK/STAT signaling pathway to treat inflammatory and autoimmune diseases, have gradually become a hot topic in the field of drug research. However, faced with a vast number of potential drug molecules, effectively screening to obtain effective drug molecules still poses a significant challenge. Therefore, by using computer-aided processing of molecular information, combined with related technologies such as molecular docking, pharmacokinetics, molecular dynamics, and machine learning, to conduct virtual screening of drug molecules, important research ideas are provided for subsequent drug synthesis and biological testing. This article mainly reviews the advancements in the research of JAK small molecule inhibitors using computer-aided drug design techniques, introduces the related design concepts, and offers a perspective on the future development of computer-aided drug design.
| [1] | Virtanen, A., Kettunen, V., Musta, K., Räkköläinen, V., Knapp, S., Haikarainen, T., et al. (2025) Molecular Basis of JAK Kinase Regulation Guiding Therapeutic Approaches: Evaluating the JAK3 Pseudokinase Domain as a Drug Target. Advances in Biological Regulation, 95, Article ID: 101072. https://doi.org/10.1016/j.jbior.2024.101072 |
| [2] | Lv, Y., Mi, P., Babon, J.J., Fan, G., Qi, J., Cao, L., et al. (2024) Small Molecule Drug Discovery Targeting the JAK-STAT Pathway. Pharmacological Research, 204, Article ID: 107217. https://doi.org/10.1016/j.phrs.2024.107217 |
| [3] | Tzeng, H., Chyuan, I. and Lai, J. (2021) Targeting the JAK-STAT Pathway in Autoimmune Diseases and Cancers: A Focus on Molecular Mechanisms and Therapeutic Potential. Biochemical Pharmacology, 193, Article ID: 114760. https://doi.org/10.1016/j.bcp.2021.114760 |
| [4] | Hu, X., li, J., Fu, M., Zhao, X. and Wang, W. (2021) The JAK/STAT Signaling Pathway: From Bench to Clinic. Signal Transduction and Targeted Therapy, 6, Article No. 402. https://doi.org/10.1038/s41392-021-00791-1 |
| [5] | Kempson, J., Ovalle, D., Guo, J., Wrobleski, S.T., Lin, S., Spergel, S.H., et al. (2017) Discovery of Highly Potent, Selective, Covalent Inhibitors of Jak3. Bioorganic & Medicinal Chemistry Letters, 27, 4622-4625. https://doi.org/10.1016/j.bmcl.2017.09.023 |
| [6] | Iznardo, H., Roé, E., Serra-Baldrich, E. and Puig, L. (2023) Efficacy and Safety of JAK1 Inhibitor Abrocitinib in Atopic Dermatitis. Pharmaceutics, 15, Article 385. https://doi.org/10.3390/pharmaceutics15020385 |
| [7] | Wrobleski, S.T., Moslin, R., Lin, S., Zhang, Y., Spergel, S., Kempson, J., et al. (2019) Highly Selective Inhibition of Tyrosine Kinase 2 (TYK2) for the Treatment of Autoimmune Diseases: Discovery of the Allosteric Inhibitor Bms-986165. Journal of Medicinal Chemistry, 62, 8973-8995. https://doi.org/10.1021/acs.jmedchem.9b00444 |
| [8] | Mohamed, M.F., Trueman, S., Othman, A.A., Han, J., Ju, T.R. and Marroum, P. (2019) Development of in Vitro—In Vivo Correlation for Upadacitinib Extended-Release Tablet Formulation. The AAPS Journal, 21, Article No. 108. https://doi.org/10.1208/s12248-019-0378-y |
| [9] | 郁倩倩, 蒋颖敏, 许磊, 等. 整合药效团及共价对接用于虚拟筛选JAK3抑制剂[J]. 化学通报(印刷版), 2021, 84(10): 1102-1107. |
| [10] | Lin, W. and Karin, M. (2007) A Cytokine-Mediated Link between Innate Immunity, Inflammation, and Cancer. Journal of Clinical Investigation, 117, 1175-1183. https://doi.org/10.1172/jci31537 |
| [11] | Sabnis, R.W. (2021) Novel Indole Compounds as TEAD Inhibitors for Treating Cancer. ACS Medicinal Chemistry Letters, 12, 1885-1886. https://doi.org/10.1021/acsmedchemlett.1c00609 |
| [12] | Lupardus, P.J., Ultsch, M., Wallweber, H., Bir Kohli, P., Johnson, A.R. and Eigenbrot, C. (2014) Structure of the Pseudokinase–kinase Domains from Protein Kinase TYK2 Reveals a Mechanism for Janus Kinase (JAK) Autoinhibition. Proceedings of the National Academy of Sciences, 111, 8025-8030. https://doi.org/10.1073/pnas.1401180111 |
| [13] | Qiu, G., Yu, L., Jia, L., Cai, Y., Chen, Y., Jin, J., et al. (2024) Identification of Novel Covalent JAK3 Inhibitors through Consensus Scoring Virtual Screening: Integration of Common Feature Pharmacophore and Covalent Docking. Molecular Diversity. https://doi.org/10.1007/s11030-024-10918-5 |
| [14] | Zhao, L., Liang, Q., He, Y., Liu, M., Tong, R., Jiang, Z., et al. (2022) HDAC/JAK Dual Target Inhibitors of Cancer-Related Targets: The Success of Nonclearable Linked Pharmacophore Mode. Bioorganic Chemistry, 129, Article ID: 106181. https://doi.org/10.1016/j.bioorg.2022.106181 |
| [15] | Bajusz, D., Ferenczy, G.G. and Keserű, G.M. (2016) Ensemble Docking-Based Virtual Screening Yields Novel Spirocyclic JAK1 Inhibitors. Journal of Molecular Graphics and Modelling, 70, 275-283. https://doi.org/10.1016/j.jmgm.2016.10.014 |
| [16] | Bono, A., La Monica, G., Alamia, F., Lauria, A. and Martorana, A. (2025) A Novel in Silico Approach for Identifying Multi-Target JAK/STAT Inhibitors as Anticancer Agents. Journal of Molecular Graphics and Modelling, 135, Article ID: 108913. https://doi.org/10.1016/j.jmgm.2024.108913 |
| [17] | Liu, J., Chen, D., Liu, H., Sha, H., Song, D., Bao, T., et al. (2018) Correction: A MicroRNA-4516 Inhibitor Sensitizes Chemo-Resistant Gastric Cancer Cells to Chemotherapy by Upregulating ING4. RSC Advances, 8, 39126-39126. https://doi.org/10.1039/c8ra90089b |
| [18] | Chen, C., Yin, Y., Shi, G., Zhou, Y., Shao, S., Wei, Y., et al. (2022) A Highly Selective JAK3 Inhibitor Is Developed for Treating Rheumatoid Arthritis by Suppressing γc Cytokine-Related JAK-STAT Signal. Science Advances, 8, eabo4363. https://doi.org/10.1126/sciadv.abo4363 |
| [19] | Bajorath, J. (2024) Milestones in Chemoinformatics: Global View of the Field. Journal of Cheminformatics, 16, Article No. 124. https://doi.org/10.1186/s13321-024-00922-0 |
| [20] | Thorarensen, A., Dowty, M.E., Banker, M.E., Juba, B., Jussif, J., Lin, T., et al. (2017) Design of a Janus Kinase 3 (JAK3) Specific Inhibitor 1-((2s, 5r)-5-((7h-Pyrrolo[2, 3-d]pyrimidin-4-Yl)amino)-2-Methylpiperidin-1-Yl)prop-2-En-1-One (PF-06651600) Allowing for the Interrogation of JAK3 Signaling in Humans. Journal of Medicinal Chemistry, 60, 1971-1993. https://doi.org/10.1021/acs.jmedchem.6b01694 |
| [21] | Wang, Y., Sun, Y., Cao, R., Liu, D., Xie, Y., Li, L., et al. (2017) In Silico Identification of a Novel Hinge-Binding Scaffold for Kinase Inhibitor Discovery. Journal of Medicinal Chemistry, 60, 8552-8564. https://doi.org/10.1021/acs.jmedchem.7b01075 |
| [22] | Reymond, J. (2001) New High-Throughput Screening Assays for Biocatalysis. CHIMIA, 55, 1049-1052. https://doi.org/10.2533/chimia.2001.1049 |
| [23] | Lavecchia, A. (2015) Machine-learning Approaches in Drug Discovery: Methods and Applications. Drug Discovery Today, 20, 318-331. https://doi.org/10.1016/j.drudis.2014.10.012 |
| [24] | Carpenter, K.A., Cohen, D.S., Jarrell, J.T. and Huang, X.D. (2018) Deep Learning and Virtual Drug Screening. Future Medicinal Chemistry, 10, 2557-2567. |
| [25] | Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K. and Kumar, P. (2021) Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery. Molecular Diversity, 25, 1315-1360. https://doi.org/10.1007/s11030-021-10217-3 |
| [26] | Arciuli, J. (2017) The Multi-Component Nature of Statistical Learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372, 20160058. https://doi.org/10.1098/rstb.2016.0058 |
| [27] | Belenahalli Shekarappa, S., Kandagalla, S. and Lee, J. (2023) Development of Machine Learning Models Based on Molecular Fingerprints for Selection of Small Molecule Inhibitors against JAK2 Protein. Journal of Computational Chemistry, 44, 1493-1504. https://doi.org/10.1002/jcc.27103 |
| [28] | He, L., Liu, J., Zhao, H., Zhang, L., Yu, R. and Kang, C. (2023) De Novo Design of Dual-Target JAK2, SMO Inhibitors Based on Deep Reinforcement Learning, Molecular Docking and Molecular Dynamics Simulations. Biochemical and Biophysical Research Communications, 638, 23-27. https://doi.org/10.1016/j.bbrc.2022.11.017 |
| [29] | Wang, Y., Gu, Y., Lou, C., Gong, Y., Wu, Z., Li, W., et al. (2022) A Multitask GNN-Based Interpretable Model for Discovery of Selective JAK Inhibitors. Journal of Cheminformatics, 14, Article No. 16. https://doi.org/10.1186/s13321-022-00593-9 |
