物理化学学报
物理化學學報
물이화학학보
Acta Physico-Chimica Sinica
2015年
9期
1795-1802
,共8页
何冰%罗勇%李秉轲%薛英%余洛汀%邱小龙%杨登贵
何冰%囉勇%李秉軻%薛英%餘洛汀%邱小龍%楊登貴
하빙%라용%리병가%설영%여락정%구소룡%양등귀
HEC1%选择性抑制剂%机器学习方法%支持向量机%随机森林%虚拟筛选
HEC1%選擇性抑製劑%機器學習方法%支持嚮量機%隨機森林%虛擬篩選
HEC1%선택성억제제%궤기학습방법%지지향량궤%수궤삼림%허의사선
HEC1%Selective inhibitor%Machine learning method%Support vector machine%Random forest%Virtual screening
HEC1(癌症高表达蛋白)是纺锤体检查点控制、着丝粒功能、细胞存活的关键的有丝分裂调节器,与原发性乳腺癌的不良预后有关。筛选具有高亲和力的HEC1新型抑制剂对探索乳腺癌的靶向治疗具有重要意义。本文从结构多样性的化合物库中筛选HEC1抑制剂。通过对分子描述符的特征筛选,采用支持向量机(SVM)和随机森林(RF)方法分别对HEC1抑制剂和非抑制剂建立了分类模型。经对比, RF模型显示了更好的预测精度。我们采用RF模型对HEC1抑制剂进行了虚拟筛选,从“in-house”实体库筛选得到2个潜在的HEC1抑制剂分子。随后对筛出的化合物进行了体外活性实验,发现对乳腺癌细胞株MDA-MB-468和MDA-MB-231均有一定程度的抗肿瘤活性。研究结果表明,机器学习方法对于设计和虚拟筛选HEC1抑制剂有良好的效果。
HEC1(癌癥高錶達蛋白)是紡錘體檢查點控製、著絲粒功能、細胞存活的關鍵的有絲分裂調節器,與原髮性乳腺癌的不良預後有關。篩選具有高親和力的HEC1新型抑製劑對探索乳腺癌的靶嚮治療具有重要意義。本文從結構多樣性的化閤物庫中篩選HEC1抑製劑。通過對分子描述符的特徵篩選,採用支持嚮量機(SVM)和隨機森林(RF)方法分彆對HEC1抑製劑和非抑製劑建立瞭分類模型。經對比, RF模型顯示瞭更好的預測精度。我們採用RF模型對HEC1抑製劑進行瞭虛擬篩選,從“in-house”實體庫篩選得到2箇潛在的HEC1抑製劑分子。隨後對篩齣的化閤物進行瞭體外活性實驗,髮現對乳腺癌細胞株MDA-MB-468和MDA-MB-231均有一定程度的抗腫瘤活性。研究結果錶明,機器學習方法對于設計和虛擬篩選HEC1抑製劑有良好的效果。
HEC1(암증고표체단백)시방추체검사점공제、착사립공능、세포존활적관건적유사분렬조절기,여원발성유선암적불량예후유관。사선구유고친화력적HEC1신형억제제대탐색유선암적파향치료구유중요의의。본문종결구다양성적화합물고중사선HEC1억제제。통과대분자묘술부적특정사선,채용지지향량궤(SVM)화수궤삼림(RF)방법분별대HEC1억제제화비억제제건립료분류모형。경대비, RF모형현시료경호적예측정도。아문채용RF모형대HEC1억제제진행료허의사선,종“in-house”실체고사선득도2개잠재적HEC1억제제분자。수후대사출적화합물진행료체외활성실험,발현대유선암세포주MDA-MB-468화MDA-MB-231균유일정정도적항종류활성。연구결과표명,궤기학습방법대우설계화허의사선HEC1억제제유량호적효과。
Highly expressed in cancer 1 (HEC1) is a conserved mitotic regulator that is critical for spindle checkpoint control, kinetochore functionality, and cel 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 predicting capability in several aspects of the diverse compounds, such as pharmacokinetics, 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 cel lines. Our study shows that ML methods are promising to design and virtualy screen inhibitors of HEC1.