Citation: HE Bing, LUO Yong, LI Bing-Ke, XUE Ying, YU Luo-Ting, QIU Xiao-Long, YANG Teng-Kuei. Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods[J]. Acta Physico-Chimica Sinica, ;2015, 31(9): 1795-1802. doi: 10.3866/PKU.WHXB201507301 shu

Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods

HE Bing ^1,2 ,
LUO Yong ¹ ,
LI Bing-Ke ² ,
XUE Ying ^1,3 ,
YU Luo-Ting ^1, ,
QIU Xiao-Long ^4,5 ,
YANG Teng-Kuei ⁴

1.
1 State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, P. R. China;
2 College of Chemistry and Life Science, Chengdu Normal University, Chengdu 611130, P. R. China;
3 College of Chemistry, Sichuan University, Chengdu 610064, P. R. China;
4 Zhaobang Bio-Med. Institute Co., Ltd., Nantong 226000, Jiangsu Province, P. R. China;
5 Wisdom Pharmaceutical Co., Ltd., Haimen 226123, Jiangsu Province, P. R. China

Received Date: 2 April 2015
Available Online: 30 July 2015
Fund Project: 抗肿瘤(乳腺癌)一类新药SKLB1312&rdquo (乳腺癌)

Highly expressed in cancer 1 (HEC1) is a conserved mitotic regulator that is critical for spindle checkpoint control, kinetochore functionality, and cell survival. Overexpression of HEC1 has been detected in a variety of human cancers, and it is linked to poor prognosis of primary breast cancers. Thus, it is important to screen novel inhibitors with high affinity for HEC1. Machine learning (ML) methods were exhibiting od pharmacodynamics, and toxicity. In this work, two ML methods, support vector machines (SVMs) and random forests (RFs), were used to develop a classification method for searching inhibitors and non-inhibitors of HEC1 from the chemical library of structural diversity by screening characteristics of molecular descriptors. Both ML methods achieved promising prediction accuracies, and the RF model showed better performance. We performed virtual screening of HEC1 inhibitors by the RF model from an in-house database to screen potential HEC1 inhibitors. Two novel potential candidates were found. In vitro experiments of the two compounds showed that both had a certain degree of antitumor activity for the MDA-MB-468 and MDA-MB-231 breast cancer cell lines. Our study shows that ML methods are promising to design and virtually screen inhibitors of HEC1.
- HEC1,
- Selective inhibitor,
- Machine learning method,
- Support vector machine,
- Random forest,
- Virtual screening
1. [1]
  
  (1) Gan, S. J.; Wang, Q.; Zhu, L. M.; Xie, H.; Ding, X. F. Basic & Clin. Med. 2015, 35 (1), 134. [甘绍举, 王青, 朱丽敏, 谢浩, 丁先锋. 基础医学与临床, 2015, 35 (1), 134.]
2. [2]
  (2) Chen, Y.; Riley, D. J.; Chen, P. L.; Lee, W. H. Mol. Cell Biol. 1997, 17 (10), 6049.
3. [3]
  (3) Du, X. L.; Wang, M. R. Acta Acad. Med. Sin. 2007, 29 (1), 137. [杜小莉, 王明荣. 中国医学科学院学报, 2007, 29 (1), 137.]
4. [4]
  (4) Hu, C. M.; Zhu, J.; Guo, X. E.; Chen, W.; Qiu, X. L.; N , B.; Chien, R.; Wang, Y. V.; Tsai, C. Y.; Wu, G.; Kim, Y.; Lopez, R.; Chamberlin, A. R.; Lee, E. H.; Lee, W. H. Oncogene 2015, 34, 1220. doi: 10.1038/onc.2014.67
5. [5]
  (5) Huang, L. Y.; Chang, C. C.; Lee, Y. S.; Chang, J. M.; Huang, J. J.; Chuang, S. H.; Kao, K. J.; Lau, G. M.; Tsai, P. Y.; Liu, C. W.; Lin, H. S.; Lau, J. Y. Mol. Cancer Ther. 2014, 13 (6), 1419.
6. [6]
  (6) Lee, Y. S.; Chuang, S. H.; Huang, L. Y.; Lai, C. L.; Lin, Y. H.; Yang, J. Y.; Liu, C. W.; Yang, S. C.; Lin, H. S.; Chang, C. C.; Lai, J. Y.; Jian, P. S.; Lam, K.; Chang, J. M.; Lau, J. Y.; Huang, J. J. J. Med. Chem. 2014, 57 (10), 4098. doi: 10.1021/jm401990s
7. [7]
  (7) Wu, G.; Qiu, X. L.; Zhou, L.; Zhu, J.; Chamberlin, R.; Lau, J.; Chen, P. L.; Lee, W. H. Cancer Res. 2008, 68 (20), 8393. doi: 10.1158/0008-5472.CAN-08-1915
8. [8]
  (8) Qiu, X. L.; Li, G.; Wu, G.; Zhu, J.; Zhou, L.; Chen, P. L.; Chamberlin, A. R.; Lee, W. H. J. Med. Chem. 2009, 52 (6), 1757. doi: 10.1021/jm8015969
9. [9]
  (9) Chen, Y.; Riley, D. J.; Zheng, L.; Chen, P. L.; Lee, W. H. J. Biol. Chem. 2002, 277 (51), 49408. doi: 10.1074/jbc.M207069200
10. [10]
  (10) Diaz-Rodríguez, E.; Sotillo, R.; Schvartzman, J. M.; Benezra, R. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (43), 16719. doi: 10.1073/pnas.0803504105
11. [11]
  (11) Ferretti, C.; Totta, P.; Fiore, M.; Mattiuzzo, M.; Schillaci, T.; Ricordye, R.; Di Leonardo, A.; Degrassi, F. Cell Cycle 2010, 9 (20), 4174. doi: 10.4161/cc.9.20.13457
12. [12]
  (12) Wei, R.; N , B.; Wu, G.; Lee, W. H. Mol. Biol. Cell 2011, 22 (19), 3584. doi: 10.1091/mbc.E11-01-0012
13. [13]
  (13) Xue, Y.; Li, H.; Ung, C.; Yap, C.; Chen, Y. Chem. Res. Toxicol. 2006, 19, 1030. doi: 10.1021/tx0600550
14. [14]
  (14) Xue, Y.; Yap, C. W.; Sun, L. Z.; Cao, Z. W.; Wang, J.; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 2004, 44, 1497. doi: 10.1021/ci049971e
15. [15]
  (15) Xue, Y.; Li, Z.; Yap, C. W.; Sun, L.; Chen, X.; Chen, Y. Z. J. Chem. Inf. Comput. Sci. 2004, 44, 1630. doi: 10.1021/ci049869h
16. [16]
  (16) Yang, X. G.; Chen, D.; Wang, M.; Xue, Y.; Chen, Y. Z. J. Comput. Chem. 2009, 30, 1202. doi: 10.1002/jcc.v30:8
17. [17]
  (17) Yang, X. G.; Lv, W.; Chen, Y. Z.; Xue, Y. J. Comput. Chem. 2010, 31, 1249.
18. [18]
  (18) Lv, W.; Xue, Y. Eur. J. Med. Chem. 2010, 45, 1167. doi: 10.1016/j.ejmech.2009.12.038
19. [19]
  (19) Cong, Y.; Yang, X.; Lv, W.; Xue, Y. J. Mol. Graph. Model. 2009, 28, 236. doi: 10.1016/j.jmgm.2009.08.001
20. [20]
  (20) Luan, F.; Liu, H.; Ma, W.; Fan, B. Eur. Med. Chem. 2008, 43, 43. doi: 10.1016/j.ejmech.2007.03.002
21. [21]
  (21) Ung, C. Y.; Li, H.; Yap, C. W.; Chen, Y. Z. Mol. Pharmacol. 2007, 71, 158.
22. [22]
  (22) Li, H.; Ung, C.; Yap, C.; Xue, Y.; Li, Z.; Cao, Z.; Chen, Y. Chem. Res. Toxicol. 2005, 18, 1071. doi: 10.1021/tx049652h
23. [23]
  (23) Li, B. K.; Cong, Y.; Tian, Z. Y.; Xue, Y. Acta Phys. -Chim. Sin. 2014, 30 (1), 171. [李秉轲, 丛湧, 田之悦, 薛英. 物理化学学报, 2014, 30 (1), 171.] doi: 10.3866/PKU.WHXB201311041
24. [24]
  (24) Huang, J. J.; Lau, J. Improved Modulators of HEC1 Activity and Methods. CN Patent 103038231.A, 2013-04-10. [Huang, J. J., Lau, J. HEC1活性调节剂及其方法: 中国, CN103038231.A[P]. 2013-04-10.]
25. [25]
  (25) Duda, R. O.; Hart, P. E. Pattern Classification and Scene Analysis; John Wiley & Sons: Hoboken, New Jersey, USA, 1973.
26. [26]
  (26) ChemDraw 7.0.1 ed.; CambridgeSoft Corporation, Cambridge: Massachusetts, USA, 2007.
27. [27]
  (27) Corina 3.4 edn.; Molecular Networks GmbH Computerchemie: Erlangen, Germany, 2006.
28. [28]
  (28) Burges, C. J. Data Min. Knowl. Disc. 1998, 2, 121.
29. [29]
  (29) Vapnik, V. N. The Nature of Statistical Learning Theory; Springer: Berlin & Heidelberg, Germany, 1995.
30. [30]
  (30) Doucet, J. P.; Barbault, F.; Xia, H.; Panaye, A.; Fan, B. Curr. Comput-Aid. Drug. 2007, 3, 263. doi: 10.2174/157340907782799372
31. [31]
  (31) Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. J. Chem. Inf. Comput. Sci. 2003, 43, 1947. doi: 10.1021/ci034160g
32. [32]
  (32) Breiman, L. Mach. Learn. 2001, 45, 5. doi: 10.1023/A: 1010933404324
33. [33]
  (33) Khandelwal, A.; Krasowski, M. D.; Reschly, E. J.; Sinz, M. W.; Swaan, P. W.; Ekins, S. Chem. Res. Toxicol. 2008, 21, 1457. doi: 10.1021/tx800102e
34. [34]
  (34) Breiman, L. Out-of-bag Estimation, 1996, http://citeseerx.ist.psu.edu.sci-hub.org/viewdoc/download?doi=10.1.1.45.3712&rep=rep1&type=pdf (accessed Mar 15, 2015).
35. [35]
  (35) Breiman, L. Wald Lecture II, Looking inside the Black Box, 2005. http://www.stat.berkeley.edu/users/breiman (accessed Mar 15, 2015).
36. [36]
  (36) Breiman, L.; Cutler, A. Random Forests, Version 5.1, 2004. http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm (accessed Mar 15, 2015).
1. [1]
  Tangsheng Guan , Ruizhi Yang , Pingyue Geng , Yuhong Lin , Shui Hu , Xiaojuan Chen , Houjin Li , Yong Shen . Innovative Digital Experiments for Glutathione-Targeted Anticancer Drugs in Redox Systems. University Chemistry, 2026, 41(1): 144-158. doi: 10.12461/PKU.DXHX202505028
2. [2]
  Hao Wu , Fengqi Li , Xinwei Shi , Haifeng Bian , Qing Zhou , Shunshun Jia , Yujie Ma , Jian Gu , Jingzi Zhang , Shuijian He , Xiangkang Meng . Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction. Acta Physico-Chimica Sinica, 2026, 42(8): 100227-0. doi: 10.1016/j.actphy.2025.100227
3. [3]
  Yihui Song , Shangshang Qin , Kai Wu , Chengyun Jin , Bin Yu . 生物化学在高水平创新型药学人才培养中的交叉融合应用——以去甲基化酶LSD1抑制剂的活性评价为例. University Chemistry, 2025, 40(6): 341-352. doi: 10.12461/PKU.DXHX202406018
4. [4]
  Peiran ZHAO , Yuqian LIU , Cheng HE , Chunying DUAN . A functionalized Eu³⁺ metal-organic framework for selective fluorescent detection of pyrene. Chinese Journal of Inorganic Chemistry, 2024, 40(4): 713-724. doi: 10.11862/CJIC.20230355
5. [5]
  Jun LUO , Baoshu LIU , Yunchang ZHANG , Bingkai WANG , Beibei GUO , Lan SHE , Tianheng CHEN . Europium(Ⅲ) metal-organic framework as a fluorescent probe for selectively and sensitively sensing Pb²⁺ in aqueous solution. Chinese Journal of Inorganic Chemistry, 2024, 40(12): 2438-2444. doi: 10.11862/CJIC.20240240
6. [6]
  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
7. [7]
  Zhi Chai , Huashan Huang , Xukai Shi , Yujing Lan , Zhentao Yuan , Hong Yan . Wittig反应的立体选择性. University Chemistry, 2025, 40(8): 192-201. doi: 10.12461/PKU.DXHX202410046
8. [8]
  Yucheng Shan , Liming Xu , Peng Sun , Zhijing Zhu , Chenglong Wang , Jinliang Li , Guang Yang , Likun Pan . A data-driven approach for rapid revealing of metal doping in MnO₂ cathodes for high-performance aqueous zinc-ion batteries. Acta Physico-Chimica Sinica, 2026, 42(7): 100232-. doi: 10.1016/j.actphy.2025.100232
9. [9]
  Shihui Shi , Haoyu Li , Shaojie Han , Yifan Yao , Siqi Liu . Regioselectively Synthesis of Halogenated Arenes via Self-Assembly and Synergistic Catalysis Strategy. University Chemistry, 2024, 39(5): 336-344. doi: 10.3866/PKU.DXHX202312002
10. [10]
  Yunhao Zhang , Yinuo Wang , Siran Wang , Dazhen Xu . Progress in Selective Construction of Functional Aromatics from Nitrogenous Cycloalkanes. University Chemistry, 2024, 39(11): 136-145. doi: 10.3866/PKU.DXHX202401083
11. [11]
  Xilin Zhao , Xingyu Tu , Zongxuan Li , Rui Dong , Bo Jiang , Zhiwei Miao . Research Progress in Enantioselective Synthesis of Axial Chiral Compounds. University Chemistry, 2024, 39(11): 158-173. doi: 10.12461/PKU.DXHX202403106
12. [12]
  Jiakun BAI , Ting XU , Lu ZHANG , Jiang PENG , Yuqiang LI , Junhui JIA . A red-emitting fluorescent probe with a large Stokes shift for selective detection of hypochlorous acid. Chinese Journal of Inorganic Chemistry, 2024, 40(6): 1095-1104. doi: 10.11862/CJIC.20240002
13. [13]
  .
  CCS Chemistry | 超分子活化底物为自由基促进高效选择性光催化氧化
  . CCS Chemistry, 2025, 7(10.31635/ccschem.025.202405229): -.
14. [14]
  Feifei Yang , Wei Zhou , Chaoran Yang , Tianyu Zhang , Yanqiang Huang . Enhanced Methanol Selectivity in CO₂ Hydrogenation by Decoration of K on MoS₂ Catalyst. Acta Physico-Chimica Sinica, 2024, 40(7): 2308017-0. doi: 10.3866/PKU.WHXB202308017
15. [15]
  Guanghuan Chen , Yinghua Wang , Jinhui Ming , Jian Ling , Zhihui Shao , Chongdao Lu , Yong Shi . 香芹酮β硼化：一个可用于展示反应选择性的教学实验. University Chemistry, 2026, 41(5): 101-108. doi: 10.12461/PKU.DXHX202510042
16. [16]
  Kaixin Lai , Ke Wang , Xinwei Tang , Tian Sun , Yan Li , Zhiwei Hu , Wei Li , Wei Hong , Wenkai Chen , Xinyi Jiang , Zicheng Wang , Tianxi Liu . Selective thermal radiation polyimide composite fabric for efficient infrared stealth and electromagnetic protection. Acta Physico-Chimica Sinica, 2026, 42(7): 100272-. doi: 10.1016/j.actphy.2026.100272
17. [17]
  Jiali CHEN , Guoxiang ZHAO , Yayu YAN , Wanting XIA , Qiaohong LI , Jian ZHANG . Machine learning exploring the adsorption of electronic gases on zeolite molecular sieves. Chinese Journal of Inorganic Chemistry, 2025, 41(1): 155-164. doi: 10.11862/CJIC.20240408
18. [18]
  Jia Zhou , Huaying Zhong . Experimental Design of Computational Materials Science Combined with Machine Learning. University Chemistry, 2025, 40(3): 171-177. doi: 10.12461/PKU.DXHX202406004
19. [19]
  Xinghai Li , Zhisen Wu , Lijing Zhang , Shengyang Tao . Machine Learning Enables the Prediction of Amide Bond Synthesis Based on Small Datasets. Acta Physico-Chimica Sinica, 2025, 41(2): 100010-0. doi: 10.3866/PKU.WHXB202309041
20. [20]
  Jian Cao , Chang Liu , Danling Wang , Haichao Li , Lina Xu , Hongping Xiao , Shaoqi Zhan , Xiao He , Guoyong Fang . Machine learning potentials for property predictions of two-dimensional group-Ⅲ nitrides. Acta Physico-Chimica Sinica, 2026, 42(4): 100224-0. doi: 10.1016/j.actphy.2025.100224

Get Citation

PDF

XML

Metrics

PDF Downloads(203)
Abstract views(1372)
HTML views(127)

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