基于分子描述符和机器学习方法预测和虚拟筛选乳腺癌靶向蛋白HEC1抑制剂

doi:10.3866/PKU.WHXB201507301

Abstract

Abstract:

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

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Bing. HE,Yong. LUO,Bing-Ke. LI,Ying. XUE,Luo-Ting. YU,Xiao-Long. QIU,Teng-Kuei. YANG. Predicting and Virtually Screening Breast Cancer Targeting Protein HEC1 Inhibitors by Molecular Descriptors and Machine Learning Methods[J].Acta Phys. -Chim. Sin., 2015, 31(9): 1795-1802.

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

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