本文選題：ADMET + hERG鉀離子通道�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：候選藥物分子藥代動(dòng)力學(xué)性質(zhì)(吸收、分布、代謝、排泄,ADME)和毒性(T)的優(yōu)劣是藥物研發(fā)是否成功的關(guān)鍵因素,因此在藥物開發(fā)的早期對(duì)化合物的ADMET進(jìn)行評(píng)價(jià)和優(yōu)化具有重要的意義。本論文擬圍繞hERG鉀離子通道和乳腺癌耐藥蛋白這兩個(gè)與ADMET密切相關(guān)的蛋白展開研究,構(gòu)建hERG鉀離子通道和乳腺癌耐藥蛋白的抑制劑預(yù)測(cè)模型,并研究受體-配體相互作用機(jī)制。阻礙hERG鉀離子通道可能會(huì)導(dǎo)致長(zhǎng)QT綜合癥、心律失常甚至猝死,因此為減少藥物潛在的心臟毒性的風(fēng)險(xiǎn)而對(duì)hERG編碼的鉀離子通道毒性預(yù)測(cè)是十分必要的。在第一部分中,我們首先構(gòu)建大量的藥效團(tuán)模型,然后使用遞歸分割方法確定了多個(gè)對(duì)hERG鉀離子通道抑制劑和非抑制劑具有最優(yōu)分類效果的藥效團(tuán)模型,最后通過機(jī)器學(xué)習(xí)方法(支持向量機(jī)、樸素貝葉斯)構(gòu)建了基于多藥效團(tuán)的分類預(yù)測(cè)模型。最優(yōu)的支持向量機(jī)模型對(duì)訓(xùn)練集的預(yù)測(cè)精度為84.7%,對(duì)測(cè)試集和外部驗(yàn)證集的預(yù)測(cè)精度均為82.1%；該模型能準(zhǔn)確預(yù)測(cè)測(cè)試集中83.6%的抑制劑和78.2%的非抑制劑。此外,我們對(duì)重要的藥效團(tuán)模型進(jìn)行了聚類分析,并通過分析具有代表性的藥效團(tuán)來描述hERG鉀離子通道與配體間的多種相互作用機(jī)制。多藥耐藥現(xiàn)象是當(dāng)今治療惡性腫瘤失敗的主要原因之一,乳腺癌耐藥蛋白在多藥耐藥性起著至關(guān)重要的作用,其過量表達(dá)可能會(huì)導(dǎo)致藥物達(dá)不到預(yù)期的療效。在第二部分中,我們首先收集了860個(gè)乳腺癌耐藥蛋白抑制劑和非抑制劑,并通過模擬退火和隨機(jī)森林方法從大量的分子描述符中進(jìn)行特征選擇,并確定了36個(gè)重要的分子描述符；然后,基于最優(yōu)描述符集和不同的分子指紋構(gòu)建了樸素貝葉斯分類預(yù)測(cè)模型。結(jié)果表明：基于最優(yōu)描述符集和分子指紋LCFP 4所構(gòu)建的分類模型達(dá)到最好的預(yù)測(cè)效果,對(duì)訓(xùn)練集的預(yù)測(cè)精度為90.1%,對(duì)測(cè)試集的預(yù)測(cè)精度為94.2%,對(duì)外部驗(yàn)證集的預(yù)測(cè)精度為93.3%。此外,我們分析了對(duì)樸素貝葉斯分類模型貢獻(xiàn)最大和最小的重要結(jié)構(gòu)片段,深入探討了BCRP抑制作用中起關(guān)鍵作用的結(jié)構(gòu)片段。
[Abstract]:The advantages and disadvantages of molecular pharmacokinetic properties (absorption, distribution, metabolism, excretion, ADMEand toxicity) of candidate drugs are key factors for the success of drug development. Therefore, it is of great significance to evaluate and optimize the ADMET of compounds in the early stage of drug development. In this paper, we study the two proteins closely related to ADMET, hERG potassium channel and breast cancer resistance protein, and construct the inhibitor prediction model of hERG potassium channel and breast cancer resistance protein, and study the mechanism of receptor-ligand interaction. Blocking the potassium channel of hERG may lead to long QT syndrome arrhythmia and even sudden death so it is necessary to predict the potassium channel toxicity encoded by hERG in order to reduce the risk of potential cardiac toxicity of the drug. In the first part, we first construct a large number of pharmacophore models, and then we use recursive segmentation method to determine a number of pharmacophore models with optimal classification effects for hERG potassium channel inhibitors and non-inhibitors. Finally, a classification and prediction model based on multi-pharmacophore is constructed by means of machine learning (support vector machine, naive Bayes). The prediction accuracy of the optimal support vector machine model for the training set is 84.7, the prediction accuracy for the test set and the external verification set is both 82.1, and the model can accurately predict 83.6% of the inhibitors and 78.2% of the non-inhibitors in the test set. In addition, the important pharmacophore models were clustered and the representative pharmacophore groups were analyzed to describe the interaction mechanisms between hERG potassium channels and ligands. Multidrug resistance (MDR) is one of the main causes of failure in the treatment of malignant tumors. Multidrug resistance protein plays an important role in MDR, and its overexpression may lead to drug failure. In the second part, we first collected 860 breast cancer resistant protein inhibitors and non-inhibitors, and used simulated annealing and random forest methods to select features from a large number of molecular descriptors. 36 important molecular descriptors are determined, and then a naive Bayesian classification and prediction model is constructed based on the optimal descriptor set and different molecular fingerprints. The results show that the classification model based on the optimal descriptor set and the molecular fingerprint LCFP _ 4 has the best prediction effect. The prediction accuracy for the training set is 90.1, for the test set is 94.2 and for the external verification set is 93.3. In addition, we analyze the important structural fragments that contribute the most and least to the naive Bayesian classification model, and discuss in depth the structural fragments that play a key role in the inhibition of BCRP.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R96

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 XUAN ShouYi;LIANG Hu;WANG Zhi;YAN AiXia;;Classification of blocker and non-blocker of hERG potassium ion channel using a support vector machine[J];Science China(Chemistry);2013年10期

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本文編號(hào)：1891003