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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm

Tai, Hio Kuan January 2018 (has links)
University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science
2

Computer-aided drug discovery and protein-ligand docking / CUHK electronic theses & dissertations collection

January 2015 (has links)
Developing a new drug costs up to US$2.6B and 13.5 years. To save money and time, we have developed a toolset for computer-aided drug discovery, and utilized our toolset to discover drugs for the treatment of cancers and influenza. / We first implemented a fast protein-ligand docking tool called idock, and obtained a substantial speedup over a popular counterpart. To facilitate the large-scale use of idock, we designed a heterogeneous web platform called istar, and collected a huge database of more than 23 million small molecules. To elucidate molecular interactions in web, we developed an interactive visualizer called iview. To synthesize novel compounds, we developed a fragment-based drug design tool called iSyn. To improve the predictive accuracy of binding affinity, we exploited the machine learning technique random forest to re-score both crystal and docked poses. To identify structurally similar compounds, we ported the ultrafast shape recognition algorithms to istar. All these tools are free and open source. / We applied our novel toolset to real world drug discovery. We repurposed anti-acne drug adapalene for the treatment of human colon cancer, and identified potential inhibitors of influenza viral proteins. Such new findings could hopefully save human lives. / 開發一種新藥需要多至26億美元和13年半的時間。為節省金錢和時間,我們開發了一套計算機輔助藥物研發工具集,並運用該工具集尋找藥物治療癌症和流感。 / 我們首先實現了一個快速的蛋白與配體對接工具idock,相比一個同類流行軟件獲得了顯著的速度提升。為輔助idock 的大規模使用,我們設計了一個異構網站平台istar,收集了多達兩千三百萬個小分子的大型數據庫。為在網頁展示分子間相互作用,我們開發了一個交互式可視化軟件iview。為生成全新的化合物,我們開發了一個基於分子片段的藥物設計工具iSyn。為改進結合強度預測的精度,我們利用了機器學習技術隨機森林去重新打分晶體及預測構象。為尋找結構相似的化合物,我們移植了超快形狀識別算法至istar。所有這些工俱全是免費和開源。 / 我們應用了此創新工具集至現實世界藥物尋找中。我們發現抗痤瘡藥阿達帕林可用於治療人類結腸癌,亦篩選出流感病毒蛋白的潛在抑制物。這些新發現可望拯救人類生命。 / Li, Hongjian. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2015. / Includes bibliographical references (leaves 340-394). / Abstracts also in Chinese. / Title from PDF title page (viewed on 15, September, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.
3

Application of neural networks in the first principles calculations and computer-aided drug design

Hu, Lihong., 胡麗紅. January 2004 (has links)
published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy
4

A computational-based drug development framework.

January 2011 (has links)
Tse, Ching Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 188-200). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Obtain information on drug targets --- p.3 / Chapter 1.2 --- Drug Design --- p.5 / Chapter 1.3 --- Interface for interaction --- p.9 / Chapter 1.4 --- Summary --- p.10 / Chapter 2 --- Background Study --- p.12 / Chapter 2.1 --- Protein Function Prediction --- p.16 / Chapter 2.2 --- Drug Design --- p.37 / Chapter 2.3 --- Visualisation and Interaction in Biomedic --- p.44 / Chapter 3 --- Overview --- p.48 / Chapter 3.1 --- Protein prediction using secondary structure analysis --- p.52 / Chapter 3.2 --- Knowledge-driven ligand design --- p.55 / Chapter 3.3 --- Interactive interface in virtual reality --- p.57 / Chapter 4 --- Protein Function Prediction --- p.60 / Chapter 4.1 --- Introduction --- p.61 / Chapter 4.1.1 --- Motivation --- p.61 / Chapter 4.1.2 --- Objective --- p.62 / Chapter 4.1.3 --- Overview --- p.63 / Chapter 4.2 --- Methods and Design --- p.66 / Chapter 4.2.1 --- Feature Cell --- p.68 / Chapter 4.2.2 --- Heterogeneous Vector --- p.71 / Chapter 4.2.3 --- Feature Cell Similarity --- p.75 / Chapter 4.2.4 --- Heterogeneous Vector Similarity --- p.79 / Chapter 4.3 --- Experiments --- p.85 / Chapter 4.3.1 --- Data Preparation --- p.85 / Chapter 4.3.2 --- Experimental Methods --- p.87 / Chapter 4.4 --- Results --- p.97 / Chapter 4.4.1 --- Scalability --- p.97 / Chapter 4.4.2 --- Cluster Quality --- p.99 / Chapter 4.4.3 --- Classification Quality --- p.102 / Chapter 4.5 --- Discussion --- p.103 / Chapter 4.6 --- Conclusion --- p.104 / Chapter 5 --- Drug Design --- p.106 / Chapter 5.1 --- Introduction --- p.107 / Chapter 5.1.1 --- Motivation --- p.107 / Chapter 5.1.2 --- Objective --- p.109 / Chapter 5.1.3 --- Overview --- p.109 / Chapter 5.2 --- Methods --- p.111 / Chapter 5.2.1 --- Fragment Joining --- p.115 / Chapter 5.2.2 --- Genetic Operators --- p.116 / Chapter 5.2.3 --- Post-Processing --- p.124 / Chapter 5.3 --- Experiments --- p.128 / Chapter 5.3.1 --- Data Preparation --- p.129 / Chapter 5.3.2 --- Experimental Methods --- p.132 / Chapter 5.4 --- Results --- p.134 / Chapter 5.4.1 --- Binding Pose --- p.134 / Chapter 5.4.2 --- Free Energy and Molecular Weight --- p.137 / Chapter 5.4.3 --- Execution Time --- p.138 / Chapter 5.4.4 --- Handling Phosphorus --- p.138 / Chapter 5.5 --- Discussions --- p.139 / Chapter 5.6 --- Conclusion --- p.140 / Chapter 6 --- Interface in Virtual Reality --- p.142 / Chapter 6.1 --- Introduction --- p.143 / Chapter 6.1.1 --- Motivation --- p.143 / Chapter 6.1.2 --- Objective --- p.145 / Chapter 6.1.3 --- Overview --- p.145 / Chapter 6.2 --- Methods and Design --- p.146 / Chapter 6.2.1 --- Hybrid Drug Synthesis --- p.147 / Chapter 6.2.2 --- Interactive Interface in Virtual Reality --- p.154 / Chapter 6.3 --- Experiments and Results --- p.171 / Chapter 6.3.1 --- Data Preparation --- p.171 / Chapter 6.3.2 --- Experimental Settings --- p.172 / Chapter 6.3.3 --- Results --- p.173 / Chapter 6.4 --- Discussions --- p.176 / Chapter 6.5 --- Conclusions --- p.179 / Chapter 7 --- Conclusion --- p.180 / A Glossary --- p.184 / Bibliography --- p.188

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