本文選題：苯酚及其衍生物　切入點(diǎn)：定量構(gòu)效關(guān)系　出處：《山西師范大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：目前，隨著合成工業(yè)的不斷發(fā)展，苯酚及其衍生物被廣泛地用作化工原料。因其具有明顯的生物毒性、內(nèi)分泌干擾性和生物富集作用，成為普遍存在的污染物，對人類及其它動植物帶來一定的危害，這就使對其生態(tài)毒性進(jìn)行風(fēng)險(xiǎn)評估的研究有了一定的價(jià)值。隨著研究的深入，為了節(jié)省時間和金錢，了解毒性機(jī)理，模擬非合成化合物的生態(tài)毒理學(xué)行為，同時滿足公眾反對動物測試的呼吁，定量結(jié)構(gòu)-毒性相關(guān)（Quantitative Structure-Toxicity Relationship，QSTR）研究在環(huán)境科學(xué)領(lǐng)域已取得了多項(xiàng)成果，，故有必要開展以QSTR方法建立苯酚及其衍生物毒性模型的研究。 在20世紀(jì)90年代后期，發(fā)現(xiàn)了一種測試生物毒性的新方法——梨形四膜蟲毒性測試法。該測試方法檢測速度快、方法操作簡單、經(jīng)濟(jì)實(shí)惠、應(yīng)用的范圍廣范，因而在藥物、有機(jī)物、無機(jī)物、水污染物等方面廣泛用于毒理學(xué)評價(jià)。本文在查閱大量文獻(xiàn)的基礎(chǔ)上，選取了258個苯酚及其衍生物對水生梨形四膜蟲的毒性數(shù)據(jù)，結(jié)合軟件篩選出的7個分子描述符作為建模的結(jié)構(gòu)參數(shù)，分別采用多元線性回歸（MultipleLinear Regression，MLR）、偏最小二乘（Partial Least Squares，PLS）、BP（BackforwardPropagation，BP）神經(jīng)網(wǎng)絡(luò)三種方法進(jìn)行定量構(gòu)效關(guān)系研究。 本文主要工作包括： （1）介紹了本文運(yùn)用的相關(guān)化學(xué)計(jì)量學(xué)方法，如多元線性回歸、偏最小二乘、BP神經(jīng)網(wǎng)絡(luò)、主成分分析（Principal Component Analysis，PCA）等的基本原理。 （2）利用ADMEWORKS ModelBuilder軟件（Version4.5Standard）計(jì)算并篩選了258個苯酚及其衍生物的分子描述符，最后選出7個描述符作為建模變量。 （3）運(yùn)用穩(wěn)健診斷方法（Robust Diagnostic Method）剔除24個奇異樣本，繼而采用球排除算法（Sphere-exclusion Algorithms）將樣本合理劃分為多個訓(xùn)練集與內(nèi)部測試集，同時要求內(nèi)部測試集和外部驗(yàn)證集均勻分布在PC空間的整個區(qū)域內(nèi)，最終劃分為3類合理的樣本。 （4）結(jié)合取自文獻(xiàn)的毒性數(shù)據(jù)，分別采用多元線性回歸、偏最小二乘、BP神經(jīng)網(wǎng)絡(luò)方法進(jìn)行QSTR研究，成功建立了毒性預(yù)測模型，并對外部驗(yàn)證集采用共識建模方法（Consensus Modeling Method），從而提高模型的預(yù)測能力。 根據(jù)QSTR研究結(jié)果分析表明，所建模型均具有較好的預(yù)測能力和穩(wěn)定性，且與MLR、PLS模型相比，BP神經(jīng)網(wǎng)絡(luò)模型性能略勝一籌，即非線性模型比線性模型性能優(yōu)越。但是BP神經(jīng)網(wǎng)絡(luò)建立的模型不能直接給出直觀的數(shù)學(xué)模型和公式，而MLR、PLS模型更為簡單明了。本文建立的QSTR預(yù)測模型，實(shí)現(xiàn)了只需知道苯酚及其衍生物的分子結(jié)構(gòu)式，而不用做實(shí)驗(yàn)就可以合理預(yù)測其毒性數(shù)據(jù)。
[Abstract]:At present, with the development of synthetic industry, phenol and its derivatives are widely used as chemical raw materials. It brings some harm to human beings and other animals and plants, which makes the study of ecological toxicity risk assessment have some value. As the research goes on, in order to save time and money, to understand the mechanism of toxicity, Mimicking the ecotoxicological behavior of non-synthetic compounds and satisfying the public's appeal against animal testing, quantitative structure-toxicity related Structure-Toxicity relationships (QSTRR) studies have made many achievements in the field of environmental science. Therefore, it is necessary to establish the toxicity model of phenol and its derivatives by QSTR method. In late 1990s, a new method for toxicity testing of Tetrahymena piriformis was discovered. The method is fast, easy to operate, economical, and widely used in drugs. Organics, inorganic substances and water pollutants are widely used in toxicological evaluation. Based on a large number of literatures, 258 toxic data of phenol and its derivatives to Tetrahymenum piriformis were selected. Using seven molecular descriptors selected by the software as the structural parameters of modeling, the quantitative structure-activity relationships were studied using three methods of multivariate linear regression and partial Least squarestio (partial Least squarestio) neural network, respectively, and BP BackforwardPropagation (BP) neural networks. The main work of this paper includes:. This paper introduces the basic principles of the related chemometrics methods, such as multivariate linear regression, partial least squares BP neural network, principal component analysis (PCA) and so on. The molecular descriptors of 258 phenol and its derivatives were calculated and screened by ADMEWORKS ModelBuilder software version 4.5 Standard, and 7 descriptors were selected as modeling variables. The robust Diagnostic method is used to eliminate 24 strange samples, and then the spherical exclusion algorithm is used to divide the samples into multiple training sets and internal test sets. At the same time, it is required that the internal test set and the external verification set are uniformly distributed in the whole area of the PC space, and finally divided into three categories of reasonable samples. In combination with the toxicity data obtained from literature, the QSTR model was successfully established by using multivariate linear regression and partial least squares BP neural network method. Consensus Modeling method is applied to the external validation set to improve the prediction ability of the model. According to the analysis of QSTR research results, the proposed model has good predictive ability and stability, and the performance of BP neural network model is better than that of MLRN model. That is, the performance of nonlinear model is superior to that of linear model, but the model established by BP neural network can not give direct mathematical model and formula, while MLR QSTR model is more simple and clear. Only the molecular structure of phenol and its derivatives is known, and the toxicity data can be reasonably predicted without experiment.
【學(xué)位授予單位】：山西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ243.12;TQ086.51

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