請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85785完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾宇鳳(Yufeng Jane Tseng) | |
| dc.contributor.author | Pei-Hua Wang | en |
| dc.contributor.author | 王珮驊 | zh_TW |
| dc.date.accessioned | 2023-03-19T23:24:19Z | - |
| dc.date.copyright | 2022-07-05 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-04-26 | |
| dc.identifier.citation | Mándi, A. Protection and Challenge of Pharmaceutical Patents. Journal of Generic Medicines 1, 72-82, doi:10.1057/palgrave.jgm.4940019 (2003). 2 Shinmori, A., Okumura, M., Marukawa, Y. & Iwayama, M. in Proceedings of the ACL-2003 workshop on Patent corpus processing-Volume 20. 56-65 (Citeseer). 3 Webber, P. M. Protecting your inventions: the patent system. Nature Reviews Drug Discovery 2, 823-830 (2003). 4 Valance, E. H. Understanding the Markush claim in chemical patents. Journal of Chemical Documentation 1, 87-92 (1961). 5 Downs, G. M. & Barnard, J. M. Chemical patent information systems. Wiley Interdisciplinary Reviews: Computational Molecular Science 1, 727-741 (2011). 6 Simmons, E. S. Prior art searching in the preparation of pharmaceutical patent applications. Drug discovery today 3, 52-60 (1998). 7 Papadatos, G. et al. SureChEMBL: a large-scale, chemically annotated patent document database. Nucleic acids research 44, D1220-D1228 (2016). 8 May, C. World Intellectual Property Organization (WIPO): Resurgence and the Development Agenda. (Routledge, 2006). 9 Hattori, K., Wakabayashi, H. & Tamaki, K. Predicting key example compounds in competitors' patent applications using structural information alone. Journal of chemical information and modeling 48, 135-142 (2008). 10 Tyrchan, C., Boström, J., Giordanetto, F., Winter, J. & Muresan, S. Exploiting structural information in patent specifications for key compound prediction. Journal of chemical information and modeling 52, 1480-1489 (2012). 11 Deng, W., Berthel, S. J. & So, W. V. Intuitive patent Markush structure visualization tool for medicinal chemists. Journal of chemical information and modeling 51, 511-520 (2011). 12 Questel Inc, 4, rue des Colonnes, 75002 Paris, France. 13 Deng, W., Scott, E., Berthel, S. J. & So, W. V. Deconvoluting complex patent Markush structures: a novel R-group numbering system. World Patent Information 34, 128-133 (2012). 14 Deng, W., Schneider, G. & So, W. V. Mapping chemical structures to Markush structures using SMIRKS. Molecular informatics 30, 665-671 (2011). 15 Kovács, P., Botka, G. & Figyelmesi, Á. Automatic generation of Markush structures from specific compounds. World Patent Information 57, 59-69 (2019). 16 Cosgrove, D. A., Green, K. M., Leach, A. G., Poirrette, A. & Winter, J. A system for encoding and searching Markush structures. Journal of chemical information and modeling 52, 1936-1947 (2012). 17 Kitch, E. W. Elementary and persistent errors in the economic analysis of intellectual property. Vand. L. Rev. 53, 1727 (2000). 18 Van Dijk, T. Patent height and competition in product improvements. The Journal of Industrial Economics, 151-167 (1996). 19 Hsu, K.-H., Su, B.-H., Tu, Y.-S., Lin, O. A. & Tseng, Y. J. Mutagenicity in a molecule: Identification of core structural features of mutagenicity using a scaffold analysis. PloS one 11, e0148900 (2016). 20 Wishart, D. S. et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research 34, D668-D672 (2006). 21 Wetzel, S. et al. Interactive exploration of chemical space with Scaffold Hunter. Nature chemical biology 5, 581-583 (2009). 22 ChemAxon. JChem. Release 15.15.14 https://www.chemaxon.com (2015). 23 Strand, D. S., Kim, D. & Peura, D. A. 25 years of proton pump inhibitors: a comprehensive review. Gut and liver 11, 27 (2017). 24 Tseng, Y.-H. & Wu, Y.-J. in Proceedings of the 1st ACM workshop on Patent information retrieval 33–36 (Association for Computing Machinery, Napa Valley, California, USA, 2008). 25 Freilich, J. Patent Clutter. Iowa L. Rev. 103, 925 (2017). 26 Hwang, F. K. & Lin, S. A simple algorithm for merging two disjoint linearly ordered sets. SIAM Journal on Computing 1, 31-39 (1972). 27 Leland, B. A. et al. Managing the combinatorial explosion. Journal of chemical information and computer sciences 37, 62-70 (1997). 28 Ehrlich, H.-C., Henzler, A. M. & Rarey, M. Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. Journal of chemical information and modeling 53, 1676-1688 (2013). 29 Sharma, N. & Ketti Ramachandran, R. The Emerging Trends of Quantum Computing Towards Data Security and Key Management. Archives of Computational Methods in Engineering, doi:10.1007/s11831-021-09578-7 (2021). 30 Lu, X., Jiang, N., Hu, H. & Ji, Z. Quantum Adder for Superposition States. International Journal of Theoretical Physics 57, 2575-2584, doi:10.1007/s10773-018-3779-2 (2018). 31 Maslov, D., Nam, Y. & Kim, J. An Outlook for Quantum Computing [Point of View]. Proceedings of the IEEE 107, 5-10, doi:10.1109/JPROC.2018.2884353 (2019). 32 Harrow, A. W. & Montanaro, A. Quantum computational supremacy. Nature 549, 203-209, doi:10.1038/nature23458 (2017). 33 Huang, H.-L., Wu, D., Fan, D. & Zhu, X. Superconducting quantum computing: a review. Science China Information Sciences 63, 1-32 (2020). 34 Kjaergaard, M. et al. Superconducting qubits: Current state of play. Annual Review of Condensed Matter Physics 11, 369-395 (2020). 35 Blatt, R. & Roos, C. F. Quantum simulations with trapped ions. Nature Physics 8, 277-284 (2012). 36 Bruzewicz, C. D., Chiaverini, J., McConnell, R. & Sage, J. M. Trapped-ion quantum computing: Progress and challenges. Applied Physics Reviews 6, 021314 (2019). 37 Slussarenko, S. & Pryde, G. J. Photonic quantum information processing: A concise review. Applied Physics Reviews 6, 041303 (2019). 38 Shor, P. W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM review 41, 303-332 (1999). 39 Grover, L. K. in Proceedings of the twenty-eighth annual ACM symposium on Theory of computing. 212-219. 40 Jin, S. et al. A query‐based quantum eigensolver. Quantum Engineering 2, e49 (2020). 41 Nielsen, M. A. & Chuang, I. (American Association of Physics Teachers, 2002). 42 Long, G.-L. Grover algorithm with zero theoretical failure rate. Physical Review A 64, 022307 (2001). 43 Castagnoli, G. Highlighting the mechanism of the quantum speedup by time-symmetric and relational quantum mechanics. Foundations of Physics 46, 360-381 (2016). 44 Toyama, F., Van Dijk, W. & Nogami, Y. Quantum search with certainty based on modified Grover algorithms: optimum choice of parameters. Quantum information processing 12, 1897-1914 (2013). 45 Salman, T. & Baram, Y. Quantum set intersection and its application to associative memory. The Journal of Machine Learning Research 13, 3177-3206 (2012). 46 Quantum Computing | IBM, <https://www.ibm.com/quantum-computing/> ( 47 Aleksandrowicz, G. et al. Qiskit: An open-source framework for quantum computing. Accessed on: Mar 16 (2019). 48 Holmes, A., Johri, S., Guerreschi, G. G., Clarke, J. S. & Matsuura, A. Y. Impact of qubit connectivity on quantum algorithm performance. Quantum Science and Technology 5, 025009 (2020). 49 Long, G.-L. & Sun, Y. Efficient scheme for initializing a quantum register with an arbitrary superposed state. Physical Review A 64, 014303 (2001). 50 Soklakov, A. N. & Schack, R. Efficient state preparation for a register of quantum bits. Physical review A 73, 012307 (2006). 51 Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018). 52 McClean, J. R., Romero, J., Babbush, R. & Aspuru-Guzik, A. The theory of variational hybrid quantum-classical algorithms. New Journal of Physics 18, 023023 (2016). 53 Peruzzo, A. et al. A variational eigenvalue solver on a photonic quantum processor. Nature communications 5, 1-7 (2014). 54 Bennett, C. H., Bernstein, E., Brassard, G. & Vazirani, U. Strengths and weaknesses of quantum computing. SIAM journal on Computing 26, 1510-1523 (1997). 55 Gil, D. & Green, W. M. in 2020 IEEE International Solid-State Circuits Conference-(ISSCC). 30-39 (IEEE). 56 Laflamme, R., Miquel, C., Paz, J. P. & Zurek, W. H. Perfect quantum error correcting code. Physical Review Letters 77, 198 (1996). 57 Preskill, J. Reliable quantum computers. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454, 385-410 (1998). 58 Aaronson, S. & Chen, L. Complexity-theoretic foundations of quantum supremacy experiments. arXiv preprint arXiv:1612.05903 (2016). 59 Boixo, S. et al. Characterizing quantum supremacy in near-term devices. Nature Physics 14, 595-600 (2018). | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85785 | - |
| dc.description.abstract | 要自動生成化學專利的草稿，首先必須草擬用來定義專利範圍的馬庫斯結構與取代基列表，目前這項工作重度仰賴有經驗的專利律師或事務所，能用的輔助工具也很少，「智慧專利」的用途便是加速這項程序。使用者只需上傳結構相似的化合物，智慧專利便會自動分析出共通的主結構並生成專利範圍的文字定義。此程式也能在所有結構變化處新增常見於過去美國銷售排行前30名的藥物專利的取代基，藉此延伸專利保護範圍。程式輸入的檔案格式為SDF檔，其中不變的核心結構和可變的取代基將分別輸出成馬庫斯結構中最初的骨架以及取代基，運算結果能下載成word(.docx)檔。「智慧專利」是能快速生成建議專利範圍的網路工具，可從網址(https://intellipatent.cmdm.tw/)免費造訪。 | zh_TW |
| dc.description.abstract | The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https ://intellipatent.cmdm.tw/. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:24:19Z (GMT). No. of bitstreams: 1 U0001-2504202221572600.pdf: 2832982 bytes, checksum: 3601e1541c5629131fd4c50a756cda24 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | ------------------------------------Part 1 CHAPTER 1. Introduction 1 1.1 Pharmaceutical composition patents 1 1.2 Markush structures 1 1.3 Related studies 4 1.4 Goal of this study 5 CHAPTER 2. Material and Methods 6 2.1 Dataset for the R group library 6 2.2 Building the R group library 6 2.2.1 R group extraction 6 2.2.2 R group categorization 6 2.3 R groups from approved drugs for comparison 7 2.4 Expanding the coverage of the Markush structure 7 2.5 Case study data 8 2.6 User’s privacy 8 CHAPTER 3. Results and Discussion 9 3.1 R group library analysis 9 3.2 R group comparison between library and real drugs 10 3.3 Case study 1: bromazine 11 3.4 Case study 2: omeprazole 15 3.5 Web server of IntelliPatent 18 CHAPTER 4. Conclusions 20 -------------------------------------Part2 CHAPTER 1. Introduction 1 1.1 Pharmaceutical patent claims 1 1.2 Patent search problem 2 1.3 Quantum computing 3 1.4 Grover’s search algorithm 4 1.5 Goal of this study 5 CHAPTER 2. Material and Methods 6 2.1 Overall workflow 6 2.2 Input data and coding table 7 2.3 MCX (Multi-Controlled-NOT) gates 7 2.4 Algorithm description 8 2.5 Oracles design 11 2.6 Simulations and experiments 12 2.7 Generalized Oracle circuit design 13 2.8 Methods to reduce the length of the quantum circuit 14 CHAPTER 3. Results 16 3.1 10-qubit proof of concept quantum simulations 16 3.2 Quantum experiments performed on a 5-qubit quantum computer 18 3.3 Analysis of the effects on different types of error on the quantum processor 20 CHAPTER 4. Discussions 22 4.1 Circuit design 22 4.2 Error types 23 4.3 Quantum-classical hybrid approach 24 4.4 Future study 25 CHAPTER 5. Conclusions 26 REFERENCES 27 APPENDIX 35 1. The list of 30 patents of the top-selling drugs in the US in 2005 35 2. The structures of 30 example compounds in the composition patent of omeprazole (EP0005129B1) 36 3. Pei-Hua Wang’s Publication List 42 | |
| dc.language.iso | en | |
| dc.subject | 網頁工具 | zh_TW |
| dc.subject | 馬庫斯結構 | zh_TW |
| dc.subject | 取代基 | zh_TW |
| dc.subject | 藥物專利 | zh_TW |
| dc.subject | 專利範圍 | zh_TW |
| dc.subject | patent claims | en |
| dc.subject | web- based tool | en |
| dc.subject | R groups | en |
| dc.subject | Markush structure | en |
| dc.subject | pharmaceutical patent | en |
| dc.title | 智能專利：快速草擬化學專利範圍的智慧系統 | zh_TW |
| dc.title | IntelliPatent: an Intelligent System for Fast Chemical Patent Claim Drafting | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.author-orcid | 0000-0003-1375-6600 | |
| dc.contributor.advisor-orcid | 曾宇鳳(0000-0002-8461-6181) | |
| dc.contributor.oralexamcommittee | 傅楸善(Chiou-Shann Fuh),陳文進(Wen-Chin Chen),趙坤茂(Kun-Mao Chao),張瑞峰(Ruey-Feng Chang) | |
| dc.contributor.oralexamcommittee-orcid | 傅楸善(0000-0002-6174-2556),趙坤茂(0000-0003-2837-1279),張瑞峰(0000-0002-2086-0097) | |
| dc.subject.keyword | 藥物專利,專利範圍,馬庫斯結構,取代基,網頁工具, | zh_TW |
| dc.subject.keyword | pharmaceutical patent,patent claims,Markush structure,R groups,web- based tool, | en |
| dc.relation.page | 43 | |
| dc.identifier.doi | 10.6342/NTU202200722 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-04-26 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-07-05 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2504202221572600.pdf | 2.77 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。