Acta Phys. -Chim. Sin. ›› 2015, Vol. 31 ›› Issue (9): 1795-1802.doi: 10.3866/PKU.WHXB201507301

• BIOPHYSICAL CHEMISTRY • Previous Articles     Next Articles

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

Bing. HE1,2,Yong. LUO1,Bing-Ke. LI2,Ying. XUE1,3,Luo-Ting. YU1(),Xiao-Long. QIU4,5,Teng-Kuei. YANG4   

  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:2015-04-02 Published:2015-09-06


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 good 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.

Key words: HEC1, Selective inhibitor, Machine learning method, Support vector machine, Random forest, Virtual screening