Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy–Antagonism in Precision Medicine
Source abstract
Abstract
Abstract Targeted drug design and development, as a core area of modern pharmaceutical research, critically depends on the assessment of protein site druggability as a fundamental component. This review systematically examines the latest research progress and application prospects of drug synergy and antagonism prediction methods that integrate protein three‐dimensional spatial structure with artificial intelligence (AI) technologies. This review showcases the molecular biological mechanisms of drug synergism vs antagonism mediated by transcription factors, signal pathway regulation, and membrane transport proteins, and subsequently delves into the molecular structural basis of protein–drug interactions, including precise identification methods for drug binding sites, optimization strategies for molecular docking techniques, and the mechanisms and structural characteristics of multi‐target drugs. The review systematically evaluates the practical application progress of AI technologies, especially machine learning and deep learning algorithms, in predicting drug synergy–antagonism effects, as well as the methodological approaches for constructing and evaluating the performance of AI prediction models that integrate multi‐source biological data. These research findings provide a solid theoretical foundation for the precision treatment of cancer, infectious diseases, and metabolic disorders, with significant clinical and translational implications for advancing personalized medicine strategies in clinical practice and facilitating the rational design and development of novel multi‐target drugs.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Computational Drug Discovery Methods · Protein Structure and Dynamics · Click Chemistry and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Donghai County People's Hospital (Affiliated Kangda College of Nanjing Medical University); Department of Oncology Zhujiang Hospital, Southern Medical University Lianyungang 222000 China
Xinling College Nantong University Nantong Jiangsu 226000 China
Department of Urology Changhai Hospital Naval Medical University (Second Military Medical University) Shanghai 200443 China
Institute of Logic and Computation TU Wien Wien 1040 Austria
Department of Biological Chemical and Pharmaceutical Sciences and Technologies University of Palermo Palermo 90123 Italy
Department of Plastic and Reconstructive Surgery Shanghai Ninth People's Hospital Shanghai JiaoTong University School of Medicine Shanghai 200011 China
Department of Oncology Zhujiang Hospital Southern Medical University Guangzhou China
Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730 China
College of Veterinary Medicine Sichuan Agricultural University Chengdu 611130 China
Department of Neurosurgery Xiangya Hospital Central South University Changsha Hunan 410008 China
National Clinical Research Center for Geriatric Disorders Xiangya Hospital Central South University Changsha 410008 China
Department of Infectious Disease and Microbiology The University of Hong Kong‐Shenzhen Hospital Shenzhen 518009 China
Department of Microbiology State Key Laboratory of Emerging Infectious Diseases Carol Yu Centre for Infection School of Clinical Medicine Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong China
Funding
National Natural Science Foundation of China
References (100+)
Show source-provided references
1. 10.1038/nrd160910.1038/nrd1609 ↗
2. 10.1038/msb.2008.5110.1038/msb.2008.51 ↗
3. 10.1016/j.chembiol.2004.07.01010.1016/j.chembiol.2004.07.010 ↗
4. 10.1038/s41746-025-01435-210.1038/s41746-025-01435-2 ↗
5. 10.1038/ng175510.1038/ng1755 ↗
6. 10.1056/NEJM19970911337110110.1056/NEJM199709113371101 ↗
7. 10.1016/j.phymed.2013.10.01610.1016/j.phymed.2013.10.016 ↗
8. 10.1016/S0140-6736(12)61080-010.1016/S0140-6736(12)61080-0 ↗
9. 10.1007/s10555-013-9451-710.1007/s10555-013-9451-7 ↗
10. 10.1371/journal.pcbi.100079610.1371/journal.pcbi.1000796 ↗
11. 10.1080/14756366.2024.238388610.1080/14756366.2024.2383886 ↗
12. 10.1016/j.ijbiomac.2024.13693310.1016/j.ijbiomac.2024.136933 ↗
13. 10.1038/s41581-024-00869-310.1038/s41581-024-00869-3 ↗
14. 10.1093/bib/bbae28110.1093/bib/bbae281 ↗
15. 10.1016/j.neuron.2018.08.01110.1016/j.neuron.2018.08.011 ↗
16. 10.1109/TNNLS.2024.347724810.1109/TNNLS.2024.3477248 ↗
17. 10.1016/j.csbj.2024.06.03710.1016/j.csbj.2024.06.037 ↗
18. 10.1016/j.cpan.2024.12.00210.1016/j.cpan.2024.12.002 ↗
19. 10.1016/j.cpan.2024.12.00110.1016/j.cpan.2024.12.001 ↗
20. 10.1016/j.csbj.2024.03.00910.1016/j.csbj.2024.03.009 ↗
21. 10.1007/s12539-023-00597-510.1007/s12539-023-00597-5 ↗
22. 10.1038/s41598-024-52814-w10.1038/s41598-024-52814-w ↗
23. 10.3389/fchem.2019.0050910.3389/fchem.2019.00509 ↗
24. Nag S. · Biotech · 2022 · 12 · 110
25. 10.1016/j.compbiomed.2024.10924510.1016/j.compbiomed.2024.109245 ↗
26. 10.1007/978-1-62703-658-0_1110.1007/978-1-62703-658-0_11 ↗
27. 10.1002/jcph.128410.1002/jcph.1284 ↗
28. 10.1007/s40262-015-0328-510.1007/s40262-015-0328-5 ↗
29. 10.1177/009127000933224710.1177/0091270009332247 ↗
30. 10.1016/j.apsb.2024.04.00610.1016/j.apsb.2024.04.006 ↗
31. 10.1080/15376516.2016.127342810.1080/15376516.2016.1273428 ↗
32. 10.1093/bib/bbab51310.1093/bib/bbab513 ↗
33. 10.1053/j.gastro.2024.02.02510.1053/j.gastro.2024.02.025 ↗
34. 10.1016/j.apsb.2024.02.02510.1016/j.apsb.2024.02.025 ↗
35. 10.1111/tpj.1588310.1111/tpj.15883 ↗
36. 10.1016/j.postharvbio.2022.11198410.1016/j.postharvbio.2022.111984 ↗
37. 10.7150/ijms.8432010.7150/ijms.84320 ↗
38. 10.1038/s41598-022-07600-x10.1038/s41598-022-07600-x ↗
39. 10.1016/j.biopha.2024.11746810.1016/j.biopha.2024.117468 ↗
40. 10.1016/j.celrep.2024.11371410.1016/j.celrep.2024.113714 ↗
41. 10.1016/j.drup.2024.10115210.1016/j.drup.2024.101152 ↗
42. 10.1016/j.jbc.2024.10785110.1016/j.jbc.2024.107851 ↗
43. 10.1038/s41467-024-53584-910.1038/s41467-024-53584-9 ↗
44. 10.1073/pnas.201026411710.1073/pnas.2010264117 ↗
45. 10.1126/science.abi900910.1126/science.abi9009 ↗
46. 10.1016/j.lfs.2018.10.04810.1016/j.lfs.2018.10.048 ↗
47. 10.2174/092986732566617112912192410.2174/0929867325666171129121924 ↗
48. 10.1038/s41598-024-54409-x10.1038/s41598-024-54409-x ↗
49. 10.1186/1477-5956-9-S1-S410.1186/1477-5956-9-S1-S4 ↗
50. 10.1038/nrg131710.1038/nrg1317 ↗
51. 10.1371/journal.pcbi.100917110.1371/journal.pcbi.1009171 ↗
52. 10.1016/j.csbj.2020.02.00810.1016/j.csbj.2020.02.008 ↗
53. 10.1093/nar/gkae110910.1093/nar/gkae1109 ↗
54. 10.1371/journal.pone.003821910.1371/journal.pone.0038219 ↗
55. 10.1093/nar/gkr43710.1093/nar/gkr437 ↗
56. 10.1093/nar/gkm29710.1093/nar/gkm297 ↗
57. 10.1093/bioinformatics/btp11610.1093/bioinformatics/btp116 ↗
58. 10.1093/bioinformatics/btn03710.1093/bioinformatics/btn037 ↗
59. 10.1073/pnas.070768410510.1073/pnas.0707684105 ↗
60. 10.1002/prot.2310310.1002/prot.23103 ↗
61. 10.1093/nar/gkp28110.1093/nar/gkp281 ↗
62. 10.1186/1471-2105-12-16010.1186/1471-2105-12-160 ↗
63. 10.1186/1472-6807-6-1910.1186/1472-6807-6-19 ↗
64. 10.1002/prot.2265510.1002/prot.22655 ↗
65. 10.1002/jcc.2125610.1002/jcc.21256 ↗
66. 10.1021/acs.jcim.9b0077810.1021/acs.jcim.9b00778 ↗
67. 10.1021/acs.jcim.2c0160810.1021/acs.jcim.2c01608 ↗
68. 10.1093/nar/gkac39410.1093/nar/gkac394 ↗
69. 10.1021/acs.jcim.4c0137210.1021/acs.jcim.4c01372 ↗
70. 10.1021/acs.jpcb.1c0091010.1021/acs.jpcb.1c00910 ↗
71. 10.1016/j.drudis.2006.11.00810.1016/j.drudis.2006.11.008 ↗
72. 10.1016/j.ejmech.2015.08.04510.1016/j.ejmech.2015.08.045 ↗
73. 10.1042/CS2012005210.1042/CS20120052 ↗
74. 10.1021/acs.jmedchem.2c0067310.1021/acs.jmedchem.2c00673 ↗
75. 10.1016/j.cell.2024.02.03410.1016/j.cell.2024.02.034 ↗
76. 10.1107/S090744499701198010.1107/S0907444997011980 ↗
77. 10.3390/ph1403023810.3390/ph14030238 ↗
78. 10.1080/07391102.2023.225208310.1080/07391102.2023.2252083 ↗
79. 10.1021/acs.jmedchem.2c0125810.1021/acs.jmedchem.2c01258 ↗
80. 10.1038/srep4130710.1038/srep41307 ↗
81. 10.1186/s12964-023-01182-510.1186/s12964-023-01182-5 ↗
82. 10.1016/j.ejps.2024.10688510.1016/j.ejps.2024.106885 ↗
83. 10.1021/ci100490w10.1021/ci100490w ↗
84. 10.1021/acs.chemrev.8b0072810.1021/acs.chemrev.8b00728 ↗
85. 10.1021/acs.jmedchem.0c0222710.1021/acs.jmedchem.0c02227 ↗
86. 10.1039/D3SC06803J10.1039/D3SC06803J ↗
87. 10.2147/DDDT.S11845710.2147/DDDT.S118457 ↗
88. Guo Y. · J. Clin. Endocrinol. Metab. · 2024 · 110
89. 10.1021/acs.jcim.4c0024710.1021/acs.jcim.4c00247 ↗
90. 10.1021/acs.jmedchem.4c0007710.1021/acs.jmedchem.4c00077 ↗
91. 10.1021/jm00049a01910.1021/jm00049a019 ↗
92. 10.1016/S0040-4020(01)86503-010.1016/S0040-4020(01)86503-0 ↗
93. 10.1080/17460441.2019.161543510.1080/17460441.2019.1615435 ↗
94. 10.1021/acs.jmedchem.8b0029510.1021/acs.jmedchem.8b00295 ↗
95. 10.1023/A:100818440355810.1023/A:1008184403558 ↗
96. 10.1021/jm030809x10.1021/jm030809x ↗
97. 10.1371/journal.pcbi.100238010.1371/journal.pcbi.1002380 ↗
98. 10.1002/minf.20200002810.1002/minf.202000028 ↗
99. 10.1016/j.csbj.2024.10.01910.1016/j.csbj.2024.10.019 ↗
100. 10.1002/ddr.2215910.1002/ddr.22159 ↗