基于中藥資源的計算機輔助藥物分子設計
發(fā)布時間：2018-07-25 12:50
【摘要】：近年來，隨著越來越多的天然產(chǎn)物成功地通過FDA認證而上市，中藥（Traditional Chinese Medicines，TCMs）作為天然產(chǎn)物的重要組成部分，在現(xiàn)代藥物研發(fā)中受到了越來越多的關注和重視。中藥用于治療疾病的主要形式是通過含有多種中草藥植物的中藥復方來實現(xiàn)的，因此人們普遍認為，中草藥可以作為藥物研發(fā)很好的類藥化合物來源。從傳統(tǒng)中草藥中尋找到相關靶點的潛在活性化合物并確定其藥理活性已經(jīng)成為制藥公司藥物開發(fā)的一個重要途徑。人們對基于中草藥資源的藥物研發(fā)已經(jīng)做過了大量的嘗試和研究，但我們對中草藥化合物的分子的性質、結構以及成藥性特征還缺乏深入的了解。此外，相比較于西藥治病理論，大部分中草藥治療疾病的機制都還不夠清晰，能否從分子水平闡述中草藥治療相關疾病的作用機制是非常重要的研究課題。最后，如何從中草藥化合物中篩選得到相關靶點的潛在活性化合物也是一個熱點研究方向。 本論文系統(tǒng)開展了基于中草藥有效成分的計算機輔助藥物分子設計研究。首先，我們系統(tǒng)比較了藥物數(shù)據(jù)庫MDDR、非藥數(shù)據(jù)庫ACD和中草藥化合物數(shù)據(jù)庫（TCMCD）中化合物的物理化學性質以及結構特征的差異。結果表明，相比MDDR和ACD，TCMCD中的化合物性質分布更為廣泛并且結構更為復雜和新穎。同時，我們發(fā)現(xiàn)基于簡單性質的類藥性預測規(guī)則預測能力較差。為了對中草藥化合物的類藥性進行定量評價，我們用機器學習方法，包括樸素貝葉斯和遞歸分割方法，構建了精確的類藥性定量預測模型。結果表明，基于分子理化性質描述符構建的類藥性模型的預測精度較低，而引入了分子指紋描述符后，類藥性模型的預測精度有了較大的提升。同時，我們發(fā)現(xiàn)類藥性模型的預測能力與訓練集的大小以及構成有著直接的關系，用所構建的最為可靠的類藥性模型對中草藥化合物數(shù)據(jù)庫進行了類藥性的評價，超過60%的中草藥化合物被預測為類藥，表明TCMCD從統(tǒng)計上講是類藥的，可以作為藥物研發(fā)的一個很好的類藥化合物來源。 中藥治療疾病主要是通過由多種中草藥植物所組成的中藥復方的形式發(fā)揮作用，因此，由大量中藥有效成分構成的中藥復方的治療疾病的機制很不清晰。為了從分子水平闡述中草藥復方治療疾病的機制，我們以治療Ⅱ型糖尿病中藥復方為例進行研究。首先，收集已知治療Ⅱ型糖尿病的中藥復方中含有的有效成分化合物以及與Ⅱ型糖尿病相關的靶點。隨后采用分子對接、藥效團映射以及機器學習的方法篩選出各靶點的潛在活性化合物。通過構建潛在活性化合物和靶點的相互作用網(wǎng)絡，從一定程度上揭示了中草藥復方治療Ⅱ型糖尿病的機制：中藥復方中的大部分有效成分只能跟單一靶點形成相互作用，構成治療Ⅱ型糖尿病的主要作用力，其次，中藥復方中的少部分化合物能和多個Ⅱ型糖尿病相關靶點作用，發(fā)揮治療糖尿病的次要作用，協(xié)同增強治療糖尿病的效果，最后，中草藥中的部分化合物不與Ⅱ型糖尿病相關靶點形成直接的相互作用，而是通過其他的一些藥理活性，，如去自由基功能/抗氧化能力、抗菌能力來協(xié)助治療糖尿病及其并發(fā)癥。所得到的這些結論能夠較好的與經(jīng)典中醫(yī)藥治病理論“君臣佐使”相吻合。 為了從中草藥化合物數(shù)據(jù)庫TCMCD中篩選得到相關靶點理想的潛在活性化合物，我們以激酶靶點ROCK1為例展開研究。考慮到蛋白柔性對虛擬篩選結果的影響，我們用機器學習方法整合ROCK1靶點多個復合物結構所得到的分子對接和藥效團模型的預測結果，構建了新穎的并行虛擬篩選策略并對其預測能力進行了評測。研究結果表明，相比較于基于單個復合物結構的分子對接或藥效團模型的預測結果，整合的虛擬篩選策略更為可靠。隨后，用構建的并行虛擬篩選策略對中草藥化合物數(shù)據(jù)庫進行了虛擬篩選，得到了53個結構新穎的ROCK1潛在活性化合物。這些化合物可以作為理想的ROCK1潛在活性化合物來進行后續(xù)的研究。所構建的并行虛擬篩選策略也可以作為一個可靠的工具用于藥物篩選。
[Abstract]:In recent years, as more and more natural products have been successfully listed by FDA certification, Traditional Chinese Medicines (TCMs), as an important component of natural products, has attracted more and more attention and attention in modern drug research and development. The main form of Chinese medicine for the treatment of diseases is through a variety of herbal plants. It is widely believed that Chinese herbal medicine can be used as a good source of drugs for drug development. It is an important way for pharmaceutical companies to develop potential active compounds from traditional Chinese herbal medicine and determine their pharmacological activities. People are on the basis of Chinese herbal medicine resources. There has been a lot of research and Research on drug research and development, but we do not know much about the properties, structure and characteristics of the molecules of Chinese herbal compounds. In addition, compared with the western medicine treatment theory, the mechanism of most Chinese herbal medicines for the treatment of diseases is not clear enough to explain the correlation of Chinese herbal medicine at the molecular level. The mechanism of the action of the disease is a very important research topic. Finally, how to screen the potential active compounds from Chinese herbal medicine compounds is also a hot research direction.
In this paper, a computer aided drug molecular design based on the effective components of Chinese herbal medicine is systematically carried out. First, we systematically compare the physical and chemical properties and structural characteristics of the compounds in the drug database MDDR, the non drug database ACD and the Chinese herbal compound database (TCMCD). The results show that compared to MDDR and ACD, TCMCD The properties of the compounds are more widely distributed and more complex and novel. At the same time, we have found that the prediction rule of the drug resistance prediction rules based on simple properties is poor. In order to evaluate the drug resistance of Chinese herbal compounds, we use machine learning methods, including the simple Juliu and the recursive segmentation method, to construct an accurate class. The results showed that the prediction accuracy of the model based on molecular physicochemical descriptors was low, and the prediction accuracy of the model was greatly improved after introducing the molecular fingerprint descriptor. At the same time, we found that the pretest ability of the drug class model and the size of the training set and the composition were straight. The relationship was evaluated with the most reliable model of drug resistance in the Chinese herbal compound database. More than 60% of the Chinese herbal compounds were predicted to be drug classes, indicating that TCMCD is a statistical class of drugs and can be used as a good source of drug class compounds in drug development.
Chinese medicine for the treatment of diseases is mainly through the form of Chinese herbal compound made up of a variety of Chinese herbal medicines. Therefore, the mechanism of the treatment of disease by a large number of effective ingredients of Chinese medicine is not clear. First, we collect effective compounds and targets related to type II diabetes, and then use molecular docking, pharmacophore mapping and machine learning to screen out potential active compounds from each target. By constructing potential active compounds and targets. The point interaction network reveals the mechanism of Chinese herbal compound treatment for type II diabetes to a certain extent: most of the effective components in the Chinese herbal compound can only interact with a single target and constitute the main force for the treatment of type II diabetes. Secondly, a few compounds in the Chinese herbal compound can be associated with multiple type of type 2 diabetes. Closing the target point to play a secondary role in the treatment of diabetes and synergistically enhance the effect of diabetes. Finally, some of the compounds in Chinese herbal medicine do not interact directly with the related targets of type 2 diabetes, but by other pharmacological activities, such as free radical function / antioxidant capacity, and antibacterial ability to assist in the treatment of diabetes. Disease and its complications. These conclusions can be better consistent with the classic Chinese medicine theory.
In order to screen the potential active compounds of the target target from the Chinese herbal compound database TCMCD, we take the kinase target ROCK1 as an example. Considering the effect of the protein flexibility on the virtual screening results, we use the machine learning method to integrate the molecular docking and the pharmacophore of multiple complex structures of the target of ROCK1. A novel parallel virtual screening strategy is constructed and its prediction ability is evaluated. The results show that the integrated virtual screening strategy is more reliable compared to the prediction results of molecular docking based on single complex structure or the model of the pharmacophore. Subsequently, the constructed parallel virtual screening strategy is used in the middle of the model. The herbal compound database has been virtual screening, and 53 novel ROCK1 potential active compounds are obtained. These compounds can be used as ideal ROCK1 potential active compounds for subsequent research. The parallel virtual screening strategy can also be used as a reliable tool for drug screening.
【學位授予單位】：蘇州大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R91-39
本文編號：2143883
[Abstract]:In recent years, as more and more natural products have been successfully listed by FDA certification, Traditional Chinese Medicines (TCMs), as an important component of natural products, has attracted more and more attention and attention in modern drug research and development. The main form of Chinese medicine for the treatment of diseases is through a variety of herbal plants. It is widely believed that Chinese herbal medicine can be used as a good source of drugs for drug development. It is an important way for pharmaceutical companies to develop potential active compounds from traditional Chinese herbal medicine and determine their pharmacological activities. People are on the basis of Chinese herbal medicine resources. There has been a lot of research and Research on drug research and development, but we do not know much about the properties, structure and characteristics of the molecules of Chinese herbal compounds. In addition, compared with the western medicine treatment theory, the mechanism of most Chinese herbal medicines for the treatment of diseases is not clear enough to explain the correlation of Chinese herbal medicine at the molecular level. The mechanism of the action of the disease is a very important research topic. Finally, how to screen the potential active compounds from Chinese herbal medicine compounds is also a hot research direction.
In this paper, a computer aided drug molecular design based on the effective components of Chinese herbal medicine is systematically carried out. First, we systematically compare the physical and chemical properties and structural characteristics of the compounds in the drug database MDDR, the non drug database ACD and the Chinese herbal compound database (TCMCD). The results show that compared to MDDR and ACD, TCMCD The properties of the compounds are more widely distributed and more complex and novel. At the same time, we have found that the prediction rule of the drug resistance prediction rules based on simple properties is poor. In order to evaluate the drug resistance of Chinese herbal compounds, we use machine learning methods, including the simple Juliu and the recursive segmentation method, to construct an accurate class. The results showed that the prediction accuracy of the model based on molecular physicochemical descriptors was low, and the prediction accuracy of the model was greatly improved after introducing the molecular fingerprint descriptor. At the same time, we found that the pretest ability of the drug class model and the size of the training set and the composition were straight. The relationship was evaluated with the most reliable model of drug resistance in the Chinese herbal compound database. More than 60% of the Chinese herbal compounds were predicted to be drug classes, indicating that TCMCD is a statistical class of drugs and can be used as a good source of drug class compounds in drug development.
Chinese medicine for the treatment of diseases is mainly through the form of Chinese herbal compound made up of a variety of Chinese herbal medicines. Therefore, the mechanism of the treatment of disease by a large number of effective ingredients of Chinese medicine is not clear. First, we collect effective compounds and targets related to type II diabetes, and then use molecular docking, pharmacophore mapping and machine learning to screen out potential active compounds from each target. By constructing potential active compounds and targets. The point interaction network reveals the mechanism of Chinese herbal compound treatment for type II diabetes to a certain extent: most of the effective components in the Chinese herbal compound can only interact with a single target and constitute the main force for the treatment of type II diabetes. Secondly, a few compounds in the Chinese herbal compound can be associated with multiple type of type 2 diabetes. Closing the target point to play a secondary role in the treatment of diabetes and synergistically enhance the effect of diabetes. Finally, some of the compounds in Chinese herbal medicine do not interact directly with the related targets of type 2 diabetes, but by other pharmacological activities, such as free radical function / antioxidant capacity, and antibacterial ability to assist in the treatment of diabetes. Disease and its complications. These conclusions can be better consistent with the classic Chinese medicine theory.
In order to screen the potential active compounds of the target target from the Chinese herbal compound database TCMCD, we take the kinase target ROCK1 as an example. Considering the effect of the protein flexibility on the virtual screening results, we use the machine learning method to integrate the molecular docking and the pharmacophore of multiple complex structures of the target of ROCK1. A novel parallel virtual screening strategy is constructed and its prediction ability is evaluated. The results show that the integrated virtual screening strategy is more reliable compared to the prediction results of molecular docking based on single complex structure or the model of the pharmacophore. Subsequently, the constructed parallel virtual screening strategy is used in the middle of the model. The herbal compound database has been virtual screening, and 53 novel ROCK1 potential active compounds are obtained. These compounds can be used as ideal ROCK1 potential active compounds for subsequent research. The parallel virtual screening strategy can also be used as a reliable tool for drug screening.
【學位授予單位】：蘇州大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R91-39
【相似文獻】
相關期刊論文 前10條
1 章承繼,李煒,仇綴百;化合物類藥性的虛擬判斷方法研究[J];中國藥學雜志;2004年09期
2 韓春艷;李燕;劉剛;;類藥性:預測與實踐[J];化學進展;2008年09期
3 劉艾林,杜冠華;化合物類藥性預測方法的研究[J];中國藥學雜志;2003年09期
4 孫文竹;楊潔;;潛在藥靶的發(fā)現(xiàn)和驗證[J];世界臨床藥物;2010年03期
5 侯永春,趙宏,孫龍川;淺析藤莖類藥性能特點[J];江西中醫(yī)藥;2004年03期
6 黃仕沛;咳嗽要言[J];新中醫(yī);1996年01期
7 李淑雯;試析根類藥性能特點[J];江西中醫(yī)藥;2002年01期
8 ;家庭常備小藥箱介紹[J];心理與健康;2005年02期
9 盛京;治哮喘加息風藥確能增效[J];四川中醫(yī);1994年05期
10 辛平;常用藥物用藥的最佳時間[J];農家參謀;1994年04期
相關會議論文 前1條
1 李曉;杜彩霞;孔德信;;生物相關性、生物相關譜用于化合物成藥性預測[A];第十一屆全國計算（機）化學學術會議論文摘要集[C];2011年
相關重要報紙文章 前3條
1 王雪飛;抗2型糖尿病新藥有苗頭[N];健康報;2004年
2 上海中醫(yī)藥大學 副教授 單寶枝;何謂中藥的“以毒攻毒”[N];家庭醫(yī)生報;2004年
3 詹建 劉亞民;補鋅補鈣不如養(yǎng)護脾胃[N];中國中醫(yī)藥報;2004年
相關博士學位論文 前2條
1 田盛;基于中藥資源的計算機輔助藥物分子設計[D];蘇州大學;2014年
2 鐘武;基于AGEs交聯(lián)結構逆轉血管硬化化合物的設計、合成和生物活性評價[D];中國人民解放軍軍事醫(yī)學科學院;2004年
相關碩士學位論文 前5條
1 呂巍;生物相關性的計算及其在候選化合物庫設計中的應用[D];山東理工大學;2007年
2 任偉;生物相關性的表征及其在藥物發(fā)現(xiàn)中的初步應用研究[D];山東理工大學;2009年
3 高大昕;野罌粟堿的合成及其中間產(chǎn)物的活性研究[D];承德醫(yī)學院;2007年
4 田盛;類藥性和生物利用度的理論預測研究[D];蘇州大學;2011年
5 王新宇;喹唑啉—嘧啶二酮衍生物的設計、合成及構效關系研究[D];青島科技大學;2011年
本文編號：2143883
本文鏈接：http://sikaile.net/yixuelunwen/yiyaoxuelunwen/2143883.html
最近更新
教材專著
