本文選題：網(wǎng)絡(luò)藥理學(xué) + 虛擬篩選　； 參考：《華東理工大學(xué)》2014年碩士論文

【摘要】：網(wǎng)絡(luò)藥理學(xué)為藥物研發(fā)帶來(lái)了新的理論指導(dǎo),其思想是構(gòu)建一個(gè)信息全面的生物信息網(wǎng)絡(luò),采用計(jì)算機(jī)技術(shù)對(duì)網(wǎng)絡(luò)進(jìn)行模擬分析,多方位考察藥物在網(wǎng)絡(luò)中的作用,降低毒副作用,提高治療效果,從而提高新藥研發(fā)的成功率,降低研發(fā)成本。 虛擬篩選采用計(jì)算機(jī)技術(shù)模擬先導(dǎo)化合物的篩選過(guò)程,從海量藥物數(shù)據(jù)庫(kù)中匹配出推薦的先導(dǎo)化合物。目前應(yīng)用廣泛的幾種虛擬篩選技術(shù)都是通過(guò)三維化學(xué)空間來(lái)分析,而很多靶標(biāo)蛋白的三維結(jié)構(gòu)都還沒(méi)有測(cè)量出來(lái),無(wú)法得以應(yīng)用。另外,目前國(guó)際上的一些主流藥物數(shù)據(jù)信息庫(kù)共享信息有限,無(wú)法構(gòu)建信息全面的藥理網(wǎng)絡(luò)。為解決這些問(wèn)題,本文首先對(duì)網(wǎng)絡(luò)藥理學(xué)研究現(xiàn)狀進(jìn)行分析,設(shè)計(jì)了一個(gè)包含國(guó)際主流藥物數(shù)據(jù)庫(kù)信息以及實(shí)驗(yàn)信息實(shí)時(shí)關(guān)聯(lián)入庫(kù)的信息全面的藥物生物化學(xué)信息庫(kù)。本文進(jìn)而分析了藥物靶標(biāo)關(guān)系預(yù)測(cè)研究現(xiàn)狀,遵循網(wǎng)絡(luò)藥理學(xué)的理論指導(dǎo),構(gòu)建基于藥物生物化學(xué)信息庫(kù)數(shù)據(jù)的藥理網(wǎng)絡(luò),提出兩種混合相似度預(yù)測(cè)方法,在構(gòu)建的藥理網(wǎng)絡(luò)中全面考慮藥物的作用,取得了更好的藥物靶標(biāo)關(guān)系預(yù)測(cè)效果。最后,本文對(duì)藥物生物信息庫(kù)數(shù)據(jù)進(jìn)行篩選,對(duì)兩種混合方法進(jìn)行驗(yàn)證分析,在處理海量藥物數(shù)據(jù)時(shí)采用Hadoop分布式平臺(tái)以提高運(yùn)算效率。
[Abstract]:Network pharmacology has brought new theoretical guidance for drug research and development. Its idea is to construct a comprehensive information biological information network, to simulate and analyze the network by computer technology, and to investigate the role of drugs in the network. Reduce toxic side effects, improve therapeutic effect, thus improve the success rate of new drug research and development, reduce R & D costs. Virtual screening simulates the screening process of lead compounds and matches the recommended lead compounds from massive drug databases. At present, several virtual screening techniques are widely used to analyze them by three-dimensional chemical space. However, many of the three-dimensional structures of target proteins have not been measured and can not be applied. In addition, some mainstream drug data databases share limited information, so it is impossible to build a comprehensive pharmacological network. In order to solve these problems, this paper first analyzes the current situation of online pharmacology research, and designs a comprehensive drug biochemistry information base which includes the international mainstream drug database information and the real-time correlation of experimental information into the database. In this paper, the current research situation of drug target relationship prediction is analyzed, and the pharmacological network based on the data of drug biochemistry information base is constructed according to the theoretical guidance of network pharmacology, and two hybrid similarity prediction methods are proposed. The effect of drug was considered in the pharmacological network, and a better prediction effect of drug target relationship was obtained. Finally, the data of drug bioinformatics database are screened, the two mixed methods are verified and analyzed, and Hadoop distributed platform is adopted to improve the computational efficiency when dealing with massive drug data.
【學(xué)位授予單位】：華東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：R96-39

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