【摘要】：血管緊張素轉(zhuǎn)換酶抑制劑(ACEI)對高血壓的治療具有重要意義。基于從結(jié)構(gòu)復(fù)雜的化合物數(shù)據(jù)庫中構(gòu)建的候選小分子數(shù)據(jù)集,采用分子對接技術(shù)從數(shù)據(jù)集中篩選出樣本構(gòu)建分類模型。分別采用支持向量機(jī)、K近鄰、決策樹、隨機(jī)森林和貝葉斯方法建立血管緊張素轉(zhuǎn)換酶潛在抑制劑和非抑制劑的分類模型。經(jīng)結(jié)果對比,支持向量機(jī)相比于其他方法有更高的預(yù)測率,其中模型總體預(yù)測率和相關(guān)系數(shù)分別為82.4%和0.653。研究表明,支持向量機(jī)方法對于虛擬篩選血管緊張素轉(zhuǎn)換酶抑制劑具有良好的效果。
[Abstract]:Angiotensin converting enzyme inhibitor (ACEI) plays an important role in the treatment of hypertension. Based on candidate small molecule data set constructed from complex compound database, molecular docking technique was used to screen samples from the data set to construct classification model. Support vector machine (SVM), K-nearest neighbor, decision tree, stochastic forest and Bayesian methods were used to establish the classification models of angiotensin converting enzyme potential inhibitors and non-inhibitors, respectively. Compared with other methods, support vector machine has a higher prediction rate, in which the overall prediction rate and correlation coefficient of the model are 82.4% and 0.653 respectively. The results show that the support vector machine method has good effect on virtual screening of angiotensin converting enzyme inhibitors.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院;江南大學(xué)數(shù)字媒體學(xué)院;江南大學(xué)工業(yè)生物技術(shù)教育部重點實驗室;
【基金】：國家自然科學(xué)基金(No.21541006)
【分類號】：R544.1;TP181
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本文編號：2359283