Citation: LÜ Wei, XUE Ying, MENG Qing-Wei. Classification Prediction of Inhibitors of H1N1 Neuraminidase by Machine Learning Methods[J]. Acta Physico-Chimica Sinica, ;2013, 29(01): 217-223. doi: 10.3866/PKU.WHXB201211122 shu

Classification Prediction of Inhibitors of H1N1 Neuraminidase by Machine Learning Methods

LÜ Wei ^1,2 ,
XUE Ying ^3,4 ,
MENG Qing-Wei ^1,2,

1.
1 College of Life Sciences, State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an, Shandong 271018, P. R. China;
2 Postdoctoral Research Bachelor of Biology, Shandong Agricultural University, Tai’an, Shandong 271018, P. R. China;
3 College of Chemistry, Key Laboratory of Green Chemistry and Technology, Ministry of Education, Sichuan University, Chengdu 610064, P. R. China;
4 State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China

Received Date: 13 September 2012
Available Online: 12 November 2012
Fund Project: 国家重点基础研究发展规划项目(973) (2009CB118500)资助 (973) (2009CB118500)

Influenza is a major respiratory infection associated with significant morbidity in the general population and mortality in elderly and high-risk patients. Research has shown that inhibiting neuraminidase (NA) prevents RNA replication, so NA is an important drug target in the treatment of H1N1 influenza virus. It is becoming increasingly important to screen and predict molecules that have NA inhibitory activity by computational methods. In this work, we explored several machine learning methods (support vector machine (SVM), k-nearest neighbor (k-NN), and C4.5 decision tree (C4.5 DT)) for predicting NA inhibitors (NAIs). These predictive systems were tested using 227 compounds (72 NAIs and 155 non-NAIs), which were significantly more diverse in chemical structure than those used in other studies. A feature selection method was used to improve the accuracy of the predictions and the selection of molecular descriptors responsible for distinguishing between NAIs and non-NAIs. The prediction accuracies were 75.9%-92.6% for all the compounds, 64.3%-78.6% for NAIs, and 77.5%-97.5% for non-NAIs. The SVM method gave the best total accuracy of 92.6% for all of methods. This work suggests that machine learning methods can be useful to predict potential NAIs from unknown sets of compounds and to determine molecular descriptors associated with NAIs.
- Machine learning method,
- H1N1 influenza virus,
- Neuraminidase inhibitor,
- Support vector machine
1. [1]
  
  (1) Erik, D. C. J. Clin. Virol. 2001, 22, 73. doi: 10.1016/S1386-6532(01)00167-6
2. [2]
  (2) Palese, P.; Tobita, K.; Ueda, M. Virology 1974, 61, 397. doi: 10.1016/0042-6822(74)90276-1
3. [3]
  (3) Moscona, A. N. Eng. J. Med. 2005, 353, 1363. doi: 10.1056/NEJMra050740
4. [4]
  (4) Erik, D. C. Nat. Rev. Drug. Disc. 2006, 5, 1015. doi: 10.1038/nrd2175
5. [5]
  (5) Schmidt, A. C. Drugs 2004, 64, 2031. doi: 10.2165/00003495-200464180-00003
6. [6]
  (6) Suzuki, Y.; Sato, K.; Kiso, M.; Hasegawa, A. Glycoconjugate J.1990, 7, 349. doi: 10.1007/BF01073378
7. [7]
  (7) Hagiwara, T.; Kijima-Suda, I.; Ido, T.; Ohrui, H.; Tomita, K.Carbohydr. Res. 1994, 263, 167. doi: 10.1016/0008-6215(94)00133-2
8. [8]
  (8) White, C. L.; Janakiraman, M. N.; Laver,W. G.; Philippon, C.Vasella, A.; Air, G. M.; Luo, M. J. Mol. Biol. 1995, 245, 623.doi: 10.1006/jmbi.1994.0051
9. [9]
  (9) Meindl, P.; Bodo, G.; Palese, P.; Schulman, J.; Tuppy, H.Virology 1974, 58, 457. doi: 10.1016/0042-6822(74)90080-4
10. [10]
  (10) Mitchell, T. Machine Learning; McGraw-Hill: New York, 1996.
11. [11]
  (11) Kohavi, R.; John, G. H. Artif. Intell. 1997, 97, 273. doi: 10.1016/S0004-3702(97)00043-X
12. [12]
  (12) Leach, A. R.; Gillet, V. J. An Introduction to Chemoinformatics;Springer: Heidelberg, 2007; p 82.
13. [13]
  (13) Yu, H.; Yang, J.;Wang,W.; Han, J. Proc. IEEE 2003, 220.
14. [14]
  (14) Furlanello, C.; Serafini, M.; Merler, S.; Jurman, G. Neural Networks 2003, 16, 641. doi: 10.1016/S0893-6080(03)00103-5
15. [15]
  (15) Lew,W.;Wu, H.W.; Mendel, D. B.; Escarpe, P. A.; Chen X.W.;Laver,W. G.; Graves, B. J.; Kim, C. U. Bioorg. Med. Chem. Lett. 1998, 8, 3321. doi: 10.1016/S0960-894X(98)00587-3
16. [16]
  (16) Sun, C.W.; Huang, H.; Feng, M. Q.; Shi, X. L.; Zhang, X. D.;Zhou, P. Bioorg. Med. Chem. Lett. 2006, 16, 162. doi: 10.1016/j.bmcl.2005.09.033
17. [17]
  (17) Wen,W. H.;Wang, S. Y.; Tsai, K. C.; Cheng, Y. S. E.; Yang, A.S.; Fang, J. M.;Wong, C. H. Bioorg. Med. Chem. 2010, 18,4074. doi: 10.1016/j.bmc.2010.04.010
18. [18]
  (18) Yeh, J. Y.; Coumar, M. S.; Horng, J. T.; Shiao, H. Y.; Kuo, F. M.;Lee, H. L.; Chen, I. C.; Chang, C.W.; Tang,W. F.; Tseng, S. N.;Chen, C. J.; Shih, S. R.; Hsu, J. T. A.; Liao, C. C.; Chao, Y. S.;Hsieh, H. P. J. Med. Chem. 2010, 53, 1519. doi: 10.1021/jm901570x
19. [19]
  (19) Lew,W.;Wu, H.W.; Chen, X.W.; Graves, B. J.; Escarpe, P. A.;MacArthur, H. L.; Mendel, D. B.; Kim, C. U. Bioorg. Med. Chem. Lett. 2000, 10, 1257. doi: 10.1016/S0960-894X(00)00214-6
20. [20]
  (20) Dao, T. T.; Tung, B. T.; Nguyen, P. H.; Thuong, P. T.; Yoo, S. S.;Kim, E. H.; Kim, S. K.; Oh,W. K. J. Nat. Prod. 2010, 73, 1636.doi: 10.1021/np1002753
21. [21]
  (21) Kolocouris, N.; Kolocouris, A.; Foscolos, G. B.; Fytas, G.;Neyts, J.; Padalko, E.; Balzarini, J.; Snoeck, R.; Andrei, G.;Clercq, E. D. J. Med. Chem. 1996, 39, 3307. doi: 10.1021/jm950891z
22. [22]
  (22) Brouillette,W. J.; Bajpai, S. N.; Ali, S. M.; Velu, S. E.;Atigadda, V. R.; Lommer, B. S.; Finley, J. B.; Luo, M.; Aird, G.M. Bioorg. Med. Chem. 2003, 11, 2739. doi: 10.1016/S0968-0896(03)00271-2
23. [23]
  (23) Liu, A. L.;Wang, H. D.; Lee, S. M. Y.;Wang, Y. T.; Du, G. H.Bioorg. Med. Chem. 2008, 16, 7141. doi: 10.1016/j.bmc.2008.06.049
24. [24]
  (24) Williams, M. A.; Lew,W.; Mendel, D. B.; Tai, C. Y.; Escarpe, P.A.; Laver,W. G.; Stevens, R. C.; Kim, C. U. Bioorg. Med. Chem. Lett. 1997, 14, 1837.
25. [25]
  (25) Zhang, L. J.;Williams, M. A.; Mendel, D. B.; Escarpe, P. A.;Kim, C. U. Bioorg. Med. Chem. Lett. 1997, 14, 1847.
26. [26]
  (26) Lv,W.; Xue, Y. Eur. J. Med. Chem. 2010, 45, 1167. doi: 10.1016/j.ejmech.2009.12.038
27. [27]
  (27) Lü,W.; Xue, Y. Acta Phys. -Chim. Sin. 2010, 26, 471.[吕巍, 薛英. 物理化学学报, 2010, 26, 471.] doi: 10.3866/PKU.WHXB20100125
28. [28]
  (28) ChemDraw, Version 9.0; Cambridge Soft Corporation:Cambridge, USA, 2004.
29. [29]
  (29) Corina, Version 3.4; Molecular Networks GmbHComputerchemie: Erlangen, Germany, 2006.
30. [30]
  (30) Hasegawa, K. J. Chem. Inf. Comput. Sci. 1999, 39, 112. doi: 10.1021/ci980088o
31. [31]
  (31) Byvatov, E.; Fechner, U.; Sadowski, J.; Schneider, G. J. Chem. Inf. Comput. Sci. 2003, 43, 1882. doi: 10.1021/ci0341161
32. [32]
  (32) He, L.; Jurs, P. C.; Custer, L. L.; Durham, S. K.; Pearl, G. M.Chem. Res. Toxicol. 2003, 16, 1567. doi: 10.1021/tx030032a
33. [33]
  (33) Lü,W.; Xue, Y. Acta Phys. -Chim. Sin. 2011, 27, 1407.[吕巍, 薛英. 物理化学学报, 2011, 27, 1407.] doi: 10.3866/PKU.WHXB20110608
34. [34]
  (34) Yang, X. G.; Lv,W.; Chen, Y. Z.; Xue, Y. J. Comput. Chem.2009, 31, 1249.
35. [35]
  (35) Lin, H. H.; Han, L. Y.; Yap, C.W.; Xue, Y.; Liu, X. H.; Zhu, F.;Chen, Y. Z. J. Mol. Graph. Model. 2007, 26, 505. doi: 10.1016/j.jmgm.2007.03.003
36. [36]
  (36) Xue, Y.; Li, H.; Ung, C. Y.; Yap, C.W.; Chen, Y. Z. Chem. Res. Toxicol. 2006, 19, 1030. doi: 10.1021/tx0600550
37. [37]
  (37) Degroeve, S.; de Baets, B.; van de Peer, Y.; Rouze, P.Bioinformatics 2002, 18, S75.
38. [38]
  (38) Garner, S. R. Weka, version 3.4.12; University ofWaikato: NewZealand, 2005.
39. [39]
  (39) Johnson, R. A.;Wichern, D.W. Applied Multivariate Statistical Analysis; Prentice Hall: New York, 1982.
40. [40]
  (40) Quinlan, J. R. C4.5, Programs for Machine Learning; MorganKaufmann: San Mateo, CA, 1992.
41. [41]
  (41) Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C. A.; Nielsen, H.Bioinformatics 2000, 16, 412. doi: 10.1093/bioinformatics/16.5.412
1. [1]
  Yuting Zhang , Zhiqian Wang . Methods and Case Studies for In-Depth Learning of the Aldol Reaction Based on Its Reversible Nature. University Chemistry, 2024, 39(7): 377-380. doi: 10.3866/PKU.DXHX202311037
2. [2]
  Jia Zhou . Constructing Potential Energy Surface of Water Molecule by Quantum Chemistry and Machine Learning: Introduction to a Comprehensive Computational Chemistry Experiment. University Chemistry, 2024, 39(3): 351-358. doi: 10.3866/PKU.DXHX202309060
3. [3]
  Yinwu Su , Xuanwen Zheng , Jianghui Du , Boda Li , Tao Wang , Zhiyan Huang . Green Synthesis of 1,3-Dibromoacetone Using Halogen Exchange Method: Recommending a Basic Organic Synthesis Teaching Experiment. University Chemistry, 2024, 39(5): 307-314. doi: 10.3866/PKU.DXHX202311092
4. [4]
  Gaofeng WANG , Shuwen SUN , Yanfei ZHAO , Lixin MENG , Bohui WEI . Structural diversity and luminescence properties of three zinc coordination polymers based on bis(4-(1H-imidazol-1-yl)phenyl)methanone. Chinese Journal of Inorganic Chemistry, 2024, 40(5): 849-856. doi: 10.11862/CJIC.20230479
5. [5]
  Hai-Yang Song , Jun Jiang , Yu-Hang Song , Min-Hang Zhou , Chao Wu , Xiang Chen , Wei-Min He . Supporting-electrolyte-free electrochemical [2 + 2 + 1] annulation of benzo[d]isothiazole 1,1-dioxides, N-arylglycines and paraformaldehyde. Chinese Chemical Letters, 2024, 35(6): 109246-. doi: 10.1016/j.cclet.2023.109246
6. [6]
  Ling-Hao Zhao , Hai-Wei Yan , Jian-Shuang Jiang , Xu Zhang , Xiang Yuan , Ya-Nan Yang , Pei-Cheng Zhang . Effective assignment of positional isomers in dimeric shikonin and its analogs by ¹H NMR spectroscopy. Chinese Chemical Letters, 2024, 35(5): 108863-. doi: 10.1016/j.cclet.2023.108863
7. [7]
  Wen-Tao Ouyang , Jun Jiang , Yan-Fang Jiang , Ting Li , Yuan-Yuan Liu , Hong-Tao Ji , Li-Juan Ou , Wei-Min He . Sono-photocatalytic amination of quinoxalin-2(1H)-ones with aliphatic amines. Chinese Chemical Letters, 2024, 35(10): 110038-. doi: 10.1016/j.cclet.2024.110038
8. [8]
  Chao LIU , Jiang WU , Zhaolei JIN . Synthesis, crystal structures, and antibacterial activities of two zinc(Ⅱ) complexes bearing 5-phenyl-1H-pyrazole group. Chinese Journal of Inorganic Chemistry, 2024, 40(10): 1986-1994. doi: 10.11862/CJIC.20240153
9. [9]
  Jiayu Huang , Kuan Chang , Qi Liu , Yameng Xie , Zhijia Song , Zhiping Zheng , Qin Kuang . Fe-N-C nanostick derived from 1D Fe-ZIFs for Electrocatalytic oxygen reduction. Chinese Journal of Structural Chemistry, 2023, 42(10): 100097-100097. doi: 10.1016/j.cjsc.2023.100097
10. [10]
  Jun Xiong , Ke-Ke Chen , Neng-Bin Xie , Wei Chen , Wen-Xuan Shao , Tong-Tong Ji , Si-Yu Yu , Yu-Qi Feng , Bi-Feng Yuan . Demethylase-assisted site-specific detection of N¹-methyladenosine in RNA. Chinese Chemical Letters, 2024, 35(5): 108953-. doi: 10.1016/j.cclet.2023.108953
11. [11]
  Peiyan Zhu , Yanyan Yang , Hui Li , Jinhua Wang , Shiqing Li . Rh(Ⅲ)‐Catalyzed sequential ring‐retentive/‐opening [4 + 2] annulations of 2H‐imidazoles towards full‐color emissive imidazo[5,1‐a]isoquinolinium salts and AIE‐active non‐symmetric 1,1′‐biisoquinolines. Chinese Chemical Letters, 2024, 35(10): 109533-. doi: 10.1016/j.cclet.2024.109533
12. [12]
  Gaofeng Zeng , Shuyu Liu , Manle Jiang , Yu Wang , Ping Xu , Lei Wang . Micro/Nanorobots for Pollution Detection and Toxic Removal. University Chemistry, 2024, 39(9): 229-234. doi: 10.12461/PKU.DXHX202311055
13. [13]
  Runze Liu , Yankai Bian , Weili Dai . Qualitative and quantitative analysis of Brønsted and Lewis acid sites in zeolites: A combined probe-assisted 1H MAS NMR and NH3-TPD investigation. Chinese Journal of Structural Chemistry, 2024, 43(4): 100250-100250. doi: 10.1016/j.cjsc.2024.100250
14. [14]
  Ke Li , Chuang Liu , Jingping Li , Guohong Wang , Kai Wang . 钛酸铋/氮化碳无机有机复合S型异质结纯水光催化产过氧化氢. Acta Physico-Chimica Sinica, 2024, 40(11): 2403009-. doi: 10.3866/PKU.WHXB202403009
15. [15]
  Fan Wu , Wenchang Tian , Jin Liu , Qiuting Zhang , YanHui Zhong , Zian Lin . Core-Shell Structured Covalent Organic Framework-Coated Silica Microspheres as Mixed-Mode Stationary Phase for High Performance Liquid Chromatography. University Chemistry, 2024, 39(11): 319-326. doi: 10.12461/PKU.DXHX202403031
16. [16]
  Wujun Jian , Mong-Feng Chiou , Yajun Li , Hongli Bao , Song Yang . Cu-catalyzed regioselective diborylation of 1,3-enynes for the efficient synthesis of 1,4-diborylated allenes. Chinese Chemical Letters, 2024, 35(5): 108980-. doi: 10.1016/j.cclet.2023.108980
17. [17]
  Jingzhao Cheng , Shiyu Gao , Bei Cheng , Kai Yang , Wang Wang , Shaowen Cao . 4-氨基-1H-咪唑-5-甲腈修饰供体-受体型氮化碳光催化剂的构建及其高效光催化产氢研究. Acta Physico-Chimica Sinica, 2024, 40(11): 2406026-. doi: 10.3866/PKU.WHXB202406026
18. [18]
  Liyang ZHANG , Dongdong YANG , Ning LI , Yuanyu YANG , Qi MA . Crystal structures, luminescent properties and Hirshfeld surface analyses of three cadmium(Ⅱ) complexes based on 2-(3-(pyridin-2-yl)-1H-pyrazol-1-yl)benzoate. Chinese Journal of Inorganic Chemistry, 2024, 40(10): 1943-1952. doi: 10.11862/CJIC.20240079
19. [19]
  Zhiwei Chen , Heyun Sheng , Xue Li , Menghan Chen , Xin Li , Qiuling Song . Efficient capture of difluorocarbene by pyridinium 1,4-zwitterionic thiolates: A concise synthesis of difluoromethylene-containing 1,4-thiazine derivatives. Chinese Chemical Letters, 2024, 35(4): 108937-. doi: 10.1016/j.cclet.2023.108937
20. [20]
  Guangming Yang , Yunhui Long . Design and Implementation of Analytical Chemistry Curriculum Based on the Learning Community of Teachers and Students. University Chemistry, 2024, 39(3): 132-137. doi: 10.3866/PKU.DXHX202309089

Get Citation

PDF

XML

Metrics

PDF Downloads(639)
Abstract views(1763)
HTML views(6)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142