跳到主要內容

臺灣博碩士論文加值系統

(44.211.117.197) 您好!臺灣時間:2024/05/23 12:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:方博靖
研究生(外文):Bo-Jing Fang
論文名稱:利用深度學習開發µ鴉片受體偏置激活劑之分子設計
論文名稱(外文):Molecular design of µ opioid receptor biased agonists through deep learning
指導教授:李豐穎
指導教授(外文):Feng-Yin Li
口試委員:廖明淵麥富德
口試日期:2021-06-25
學位類別:碩士
校院名稱:國立中興大學
系所名稱:化學系所
學門:自然科學學門
學類:化學學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:73
中文關鍵詞:µ型鴉片受體深度學習µ型鴉片偏置激活劑
外文關鍵詞:µ opioid receptorDeep learningµ opioid receptor biased agonist
相關次數:
  • 被引用被引用:0
  • 點閱點閱:102
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
µ型鴉片受體完全激動劑在鎮痛上有良好的效果,但伴隨著成癮、便祕、呼吸抑制等副作用。本論文的目的是藉由結構修飾以減少副作用。為了建構新穎的偏置激活劑,人工智慧方法變得有吸引力。然而此方法有個潛在問題,即目前可用數據集十分有限,這阻礙了深度學習訓練成功的機率。為了避免這個問題,我們先利用條件對抗正則化自動編碼器(CARAE)模型結合分子對接模擬,生成以現有的µ型鴉片偏置激活劑為基準的相關衍生物。這個方法維持模型提取的特徵,通過能規範分子屬性的CARAE模型將所需分子屬性結構插入學習所獲得的潛在空間以生成可能具有偏置激活劑特徵的分子,由此可以有效繞過低數據量的問題。在產生足夠的樣品結構以形成結構資料庫後,再利用定量構效關係模型結合人工神經網路(ANN-QSAR)和Autodock Vina輔助藥物設計方法進行藥物開發研究。
Full agonists of µ opioid receptor had brilliant performance toward analgesia, but they shared the side effects of addiction, constipation, and respiratory depression at the same time. The aim of this thesis is to reduce unwanted side effects through structural modification to produce so-called biased agonists. In order to generate novel drug leads for the biased agonists, the artificial intelligent approach, especially deep learning model, becomes attractive. However, there is one potential problem in implementing this approach, i.e., the limited dataset, which prevent the model training successfully. To circumvent this problem, generation of current µ opioid receptor biased agonist derivative combined conditional adversarially regularized autoencoder (CARAE) model is employed. The issue of low data samples can be effectively resolved by using CARAE model with the desired properties inserted into the generative latent space for generating desired structure. After producing enough biased agonist sample structures to form the biased agonist library, which are then investigated with ANN-QSAR model and Autodock Vina.
摘要 i
Abstract ii
目次 iii
表目次 vi
圖目次 vii
第一章 1
1.1 嗎啡類藥物背景 1
1.1.1 內源性鴉片肽(endogenous opioid peptide) 2
1.1.2 鴉片受體( opioid receptor) 3
1.2 嗎啡類藥物功能 4
1.2.1神經元傳遞訊息(Neurons communicate) 4
1.2.2突觸前抑制(Presynaptic inhibition) 5
1.2.3突觸後抑制(Postsynaptic inhibition) 5
1.2.4抑制環腺苷酸合成(Inhibit cAMP synthesis) 6
1.3 µ 鴉片受體的激活機制 7
1.3.1 常見激活途徑的關鍵步驟 10
1.3.2 常見的激活途徑誘導跨模螺旋變化 11
1.4 µ鴉片受體募集β–制動素(β-arrestin)的機制 12
1.4.1 µ鴉片受體全激活劑調節β-arrestin訊號的機制 12
1.5 偏置激活劑TRV130的背景與在µ鴉片受體的機制 13
1.6深度學習在化學的興起 15
1.7 深度學習與人工神經網路 16
1.8 激活函數(activation function) 17
1.9 損失函數 (Loss function) 18
1.10 梯度下降(Gradient descent) 19
1.11 反向傳播法(back propagation) 20
1.12 資料處理(Data processing) 21
1.13 分子生成模型 (Molecular Generation Model ) 23
1.14 QSAR模型結合深度學習 (QSAR model with Deep learning) 24
第二章 方法 25
2.1 分子設計的工作流程 25
2.2 資料集 27
2.3 自條件對抗性正則化的自動編碼器 ( Conditional Adversarially Regularized Autoencoders, CARAE) 27
2.3.1 CARAE模型 27
2.3.2 長短期記憶網路(Long Short-Term Memory Network: LSTM) 28
2.3.3 CARAE模型的原理及演算法 31
2.4分子對接(Molecular Docking) 33
2.4.1柔性配體與剛性受體 (Flexible ligand and rigid receptor docking) 33
2.4.2 對接工具Autodock Vina 34
2.5 QSAR模型結合深度學習 35
2.5.1 QSAR資料集 35
2.5.2分子指紋生成(fingerprint generation) 36
2.5.3 QSAR 人工神經網路模型 (QSAR Artificial Neural Network model, QSAR ANN model ) 36
2.5.4 ANN-QSAR預測能力的評估方式 37
第三章 結果與討論 38
3.1 µ鴉片受體偏置激活劑的設計 38
3.2 對接構象分析 39
3.3 ANN-QSAR 模型的有效性 48
3.4 生成多樣性下降 49
3.5生成分子的共同骨架 52
3.6 透過屬性修飾分子 53
3.6.1 嗎啡的分子屬性 logP : 1.4 TPSA: 53 54
3.6.2 吩肽尼的分子屬性 logP : 3.8 TPSA: 24 56
3.6.3 PZM21的分子屬性 logP : 3.8 TPSA: 24 57
3.6.4 Mitragynine pseudoindoxyl的分子屬性 logP : 3.2 TPSA: 64 58
3.6.5 Dihydrocodeine的分子屬性 logP : 2.1 TPSA: 42 59
第四章 結論 61
參考書目 62
1.Wilson, W. & Kuhn, C. & Swartzwelder, S. & Wilson, L. H. & Foster, J. (2013). Buzzed: The Straight Facts About the Most Used and Abused Drugs from Alcohol to Ecstasy. Taiwan:Common Master Press
2.Madariaga-Mazón, A. & Marmolejo-Valencia, A. F. & Li, Y. & Toll, Lawrence. & Houghten, R. A. & Martinez-Mayorga, K. (2017). Mu-Opioid receptor biased ligands: A safer and painless discovery of analgesics?. Drug Discovery Today, 22 (11), 1719-1729. doi: 10.1016/j.drudis.2017.07.002.
3.Chen, S. R. (2015). Discovery of novel tetrahydroisoquinoline-based μ/κ opioid receptor agonists. Unpublished master dissertation, Institute of Biotechnology, Tsinghua University, Hsinchu City.
4.Krishnamurti, C. & Rao, SSC. C. (2016). The isolation of morphine by Serturner. Indian J Anaesth, 60 (11), 861-862. doi: 10.4103/0019-5049.193696
5.Lord, J. A. H. & Waterfield, A. A. & Hughes, J. & Kosterlitz, H. W. (1997). Endogenous opioid peptides: multiple agonists and receptors. Nature, 267, 495-499.
6.Cesselin, F. (1995). Opioid and anti-opioid peptides. Fundam Clin Pharmacol, 9 (5), 409-433. doi: 10.1111/j.1472-8206.1995.tb00517.x
7.Lee, M. & Wardlaw, S. L. (2007). Beta-Endorphin*. Encyclopedia of Stress (Second Edition), 332-335. doi: 10.1016/B978-012373947-6.00055-6
8.Sprouse-Blum, A. S. & Smith, G. & Sugai, D. & Parsa, F. D. (2010). Understanding endorphins and their importance in pain management. Hawaii Med J, 69 (3), 70-71.
9.WOODS, L. A. (1956). THE PHARMACOLOGY OF NALORPHINE (N-ALLYLNORMORPHINE). Pharmacological Reviews, 8 (2), 175-198.
10.Pert, C. B. & Snyder, S. H. (1973). Opiate Receptor: Demonstration in Nervous Tissue. SCIENCE, 179 (4077), 1011-1014. doi: 10.1126/science.179.4077.1011
11.Mollereau, C. & Parmentier, M. & Mailleux, P. & Butour, J-L. & Moisand, C. & Chalon, P. & Caput, D. & Vassart, G. & Meunier, J-C. (1994). ORL1, a novel member of the opioid receptor family: Cloning, functional expression and localization. FEBS Letters, 341 (1), 33-38. doi: 10.1016/0014-5793(94)80235-1
12.Huang, W. & Manglik, A. & Venkatakrishnan, A. J. & Laeremans, T. & Feinberg, E. N. & Sanborn, A. L. & Kato, H. E. & Livingston, K. E. & Thorsen, T. S. & Kling, R. C. & Granier, S. & Gmeiner, P. & Husbands, S. M. & Traynor, J. R. & Weis, W. I. & Steyaert, J. & Dror, R. O. & Kobilka, B. K. (2015). Structural insights into µ-opioid receptor activation. Nature, 524, 315-321. doi: 10.1038/nature14886
13.Cuitavi, J. & Hipólito, L. & Canals, M. (2021). The Life Cycle of the Mu-Opioid Receptor. Trends in Biochemical Sciences, 46 (4), 315-328. doi: 10.1016/j.tibs.2020.10.002
14.Granier, S. & Manglik, A. & Kruse, A. C. & Kobilka, T. S. & Thian, F. S. & Weis, W. I. & Kobilka, B. K. (2012). Structure of the δ-opioid receptor bound to naltrindole. Nature, 485, 400-404. doi: 10.1038/nature11111
15.Snyder, L. M. & Chiang, M. C. & Loeza-Alcocer, E. & Omori, Y. & Hachisuka, J. & Sheahan, T. D. & Gale, J. R. & Adelman, P. C. & Sypek, E. I. & Fulton, S. A. & Friedman, R. L. & Wright, M. C. & Duque, M. G. & Lee, Y. S. & Hu, Z. & Huang, H. & Cai, X. & Meerschaert, K. A. & Nagarajan, V. & Hirai, T. & Scherrer, G. & Kaplan, D. H. & Porreca, F. & Davis, B. M. & Gold, M. S. & Koerber, H. R. & Ross, S. E. (2018). Kappa Opioid Receptor Distribution and Function in Primary Afferents. Neuron, 99 (6), 1274-1288. doi: 10.1016/j.neuron.2018.08.044
16.Toll, L. & Bruchas, M. R. & Calo', G. & Cox, B. M. & Zaveri, N. T. (2016). Nociceptin/Orphanin FQ Receptor Structure, Signaling, Ligands, Functions, and Interactions with Opioid Systems. Pharmacol Rev, 68 (2), 419-457. doi: 10.1124/pr.114.009209
17.Schiller, P. W. (2010). Bi- or multifunctional opioid peptide drugs. Life Sciences, 86 (15-16), 598-603. doi: 10.1016/j.lfs.2009.02.025
18.Neumeyer, J. L. & Mello, N. K. & Negus, S. S. & Bidlack, J. M. (2000). Kappa opioid agonists as targets for pharmacotherapies in cocaine abuse. Pharm Acta Helv, 74 (2-3), 337-344. doi: 10.1016/s0031-6865(99)00044-8
19.Calo, G. & Lambert, D. G. (2018). Nociceptin/orphanin FQ receptor ligands and translational challenges: focus on cebranopadol as an innovative analgesic. Br J Anaesth, 121 (5), 1105-1114. doi: 10.1016/j.bja.2018.06.024
20.Rainville, P. (2002). Brain mechanisms of pain affect and pain modulation. Current Opinion in Neurobiology, 12 (2), 195-204. doi:10.1016/S0959-4388(02)00313-6
21.Alford, S. & Schwartz, E. (2009). Presynaptic Inhibition. Encyclopedia of Neuroscience,1001-1006. doi:10.1016/B978-008045046-9.00814-7
22.Eccles, J. C.(1964).The Postsynaptic Electrical Events Produced by Chemically Transmitting Inhibitory Synapses. The Physiology of Synapses, 152-172. doi:10.1007/978-3-642-64950-9_10
23.Raker, V. K. & Becker, C. & Steinbrink, K. (2016). The CAMP Pathway as Therapeutic Target in Autoimmune and Inflammatory Diseases. Front Immunol, 7, 123. doi: 10.3389/fimmu.2016.00123
24.JM, B. & JL, T. & L, S. (2002). Section 15.1Seven-Transmembrane-Helix Receptors Change Conformation in Response to Ligand Binding and Activate G Proteins. Biochemistry. 5th edition,
25.Zhou, Q. & Yang, D. & Wu, D. & Zhong, L. & Cai, X. & Dai, A. & Shakhnovich, E. I. & Liu, Z. J. & Stevens, R. C. & Lambert, N. A. & Babu, M. M. & Wang, M. W. & Zhao, S. (2019). Common activation mechanism of class A GPCRs. 8, e50279. doi:10.7554/eLife.50279
26.Raehal, K. M. & Walker, J. L. & Bohn, L. M. (2005). Morphine Side Effects in β-Arrestin 2 Knockout Mice. Journal of Pharmacology and Experimental Therapeutics, 314 (3), 1195-1201. doi:10.1124/jpet.105.087254
27.de Waal, P. W. & Shi, J. & You, E. & Wang, X. & Melcher, K. & Jiang, Y. & Xu, H. E. & Dickson, B. M.(2020).Molecular mechanisms of fentanyl mediated β-arrestin biased signaling. PLOS Computational Biology, 16(4), e1007394.doi:10.1371/journal.pcbi.1007394
28.Manglik, A. & Lin, H. & Aryal, D. K. & McCorvy, J. D. & Dengler, D. & Corder, D. & Levit, A. & Kling, R. C. & Bernat, V. & Hübner, H. & Huang, X. & Sassano, M. F. & Giguère, P. M. & Löber, S. & Duan, D. & Scherrer, G. & Kobilka, B. K. & Gmeiner, P. & Roth, B. L. & Shoichet, B. K. (2016). Structure–based discovery of opioid analgesics with reduced side effects. Nature, 537 (7619), 185–190. doi: 10.1038/nature19112
29.Lambert, D. & Calo, G. (2020). Approval of oliceridine (TRV130) for intravenous use in moderate to severe pain in adults. British Journal of Anaesthesia, 125 (6), E473-E474. doi: 10.1016/j.bja.2020.09.021
30.Mafi, A. & Kim, S. & Goddard III, W.A. (2020). Mechanism of β-arrestin recruitment by the μ-opioid G protein-coupled receptor. PNAS, 117 (28), 16346-16355. doi:10.1073/pnas.1918264117
31.Chen, H. & Engkvist, O. & Wang, Y. & Olivecrona, M. & Blaschke, M. (2018). The rise of deep learning in drug discovery. Drug Discov Today, 23 (6), 1241-1250. doi: 10.1016/j.drudis.2018.01.039
32.Zhavoronkov, A. (2018). Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry. Molecular Pharmaceutics, 15 (10), 4311-4313.doi:acs.molpharmaceut.8b00930
33.McCulloch, W.S. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133. doi:10.1007/BF02478259
34.Bakr, M. H. & Negm, M. H. (2012). Chapter Three - Modeling and Design of High-Frequency Structures Using Artificial Neural Networks and Space Mapping. Advances in Imaging and Electron Physics, 174, 223-260. doi:10.1016/B978-0-12-394298-2.00003-X
35.涌井良幸 & 涌井貞美. (2020). ディープラーニングがわかる数学入門. Taiwan:DrMaster
36.Hochreiter, S. & Younger, A. S. & Conwell, P. R. (2001).Learning to Learn Using Gradient Descent. Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, 2130, 87-94. doi:10.1007/3-540-44668-0_13
37.Du, S. & Lee, J. & Li, H. & Wang, L. & Zhai, X. (2019). Gradient Descent Finds Global Minima of Deep Neural Networks, Proceedings of the 36th International Conference on Machine Learning, 97, 1675-1685.
38.Li, X. & Fourches, D. (2021). SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning. J. Chem. Inf. Model, 61 (4), 1560-1569. doi:10.1021/acs.jcim.0c01127
39.Rogers, D. & Hahn, M. (2010). Extended-Connectivity Fingerprints. J. Chem. Inf. Model, 50 (5), 742-754. doi:10.1021/ci100050t
40.Schneider, P. & Schneider, G. (2016). De Novo Design at the Edge of Chaos. J. Med. Chem, 59 (9), 4077-4086. doi:10.1021/acs.jmedchem.5b01849
41.Reymond, J. & Ruddigkeit, L. & Blum, L. & Deursen, R.V. (2012). Advanced ReviewThe enumeration of chemicalspace. WIREs Comput Mol Sci, 2, 717-733. doi:10.1002/wcms.1104
42.Segler, M. H. S. & Kogej, T. & Tyrchan, C. & Waller, M. P. (2018). Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci, 4 (1), 120-131. doi:10.1021/acscentsci.7b00512
43.Tropsha, A. (2010). Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics, 29 (6-7), 476-488. doi:10.1002/minf.201000061
44.Gaulton, A. & Bellis, B. J. & Bento, A. P. & Chambers, J. & Davies, M. & Hersey, A. & Lihgt, Y. & McGlinchey, S. & Michalovich, D. & Al-Lazikani, B. & Overington, J. P. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Research, 40 (D1), D1100-D1107. doi:10.1093/nar/gkr777
45.Kim, S. & Thiessen, P. A. & Bolton, E. E. & Chen, J. & Fu, G. & Asta, G. & Han, L. & He, J. & Shoemaker, B. A. & Wang, J. & Yu, B & Zhang, J. & Bryant, S. H. (2016). PubChem Substance and Compound databases. Nucleic Acids Res, 44 (D1), D1202-1213. doi: 10.1093/nar/gkv951. Epub 2015 Sep 22
46.Hu, S. & Chen, P. & Gu, P. & Wang, B. (2020). A Deep Learning-Based Chemical System for QSAR Prediction. IEEE Journal of Biomedical and Health Informatics, 24 (10), 3020 - 3028. doi:10.1109/JBHI.2020.2977009
47.Sturm, N & Mayr, A. & Van, T. L. & Chupakhin, V. & Ceulemans, H. & Wegner, J. & Golib-Dzib, J.-S. & Jeliazkova, N. & Vandriessche, Y. & Böhm, S. & Cima, V. & Martinovic, J. & Greene, N. & Aa, T. V. & Ashby, T. J. & Hochreiter, S. & Engkvist, O. & Klambauer, G. & Chen, H. (2020). Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics, 12 (26), doi:10.1186/s13321-020-00428-5
48.Shi, T. & Huang, S. & Chen, L. & Heng, Y. & Kuang, Z. & Xu, L. & Mei, H. (2020). A molecular generative model of ADAM10 inhibitors by using GRU-based deep neural network and transfer learning. Chemometrics and Intelligent Laboratory Systems, 205, 104122. doi: 10.1016/j.chemolab.2020.104122
49.Bhal, S. K. & Kassam, K. & Peirson, I. G. & Pearl, G.M. (2007). The Rule of Five Revisited: Applying Log D in Place of Log P in Drug-Likeness Filters. Mol. Pharmaceutics, 4 (4), 556-560. doi:10.1021/mp0700209
50.Ertl, P. & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of Cheminformatics, 1 (8), doi:10.1186/1758-2946-1-8
51.Ertl, P. & Rohde, B. & Selzer, P. (2000). Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J. Med. Chem, 43 (20), 3714–3717. doi:10.1021/jm000942e
52.Mysinger, M. M. & Carchia, M. & Irwin, J. J. & Shoichet, B. K. (2012). Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. J. Med. Chem. 55 (14), 6582–6594. doi:10.1021/jm300687e
53.Hong, S. H. & Ryu, S. & Lim, J. & Kim, W. Y. (2020). Molecular Generative Model Based on an Adversarially Regularized Autoencoder. Journal of Chemical Information and Modeling, 60 (1), 29-36. doi:10.1021/acs.jcim.9b00694
54.Rashid, K. M. & Louis, J. (2009). Times-series data augmentation and deep learning for construction equipment activity recognition. Advanced Engineering Informatics, 42, 100944. doi:10.1016/j.aei.2019.100944
55.Segler, M. H. S. & Kogej, T. & Tyrchan, C. & Waller, M. P. (2018). Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent. Sci, 4 (1), 120-131. doi:10.1021/acscentsci.7b00512
56.Tanioka, H. & Xin, K. (2019). いちばんやさしい ディープラーニング入門教室. Taiwan:Faces Publishing
57.Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9 (8), 1735-1780. doi: 10.1162/neco.1997.9.8.1735
58.Zhumagambetov, R. & Kazbek, D. & Shakipov, M. & Maksut, D. & Peshkov, V.A. & Fazli, S. (2020). cheML.io: an online database of ML-generated molecules. RSC Adv, 10 (73), 45189-45198. doi: 10.1039/D0RA07820
59.Langr, J. & Bok, V. (2020). GANs in Action: Deep learning with Generative Adversarial Networks. Taiwan: FLAG TECHNOLOGY
60.Arjovsky, M. & Chintala, S. & Bottou, L. (2017). Wasserstein generative adversarial networks. ICML'17: Proceedings of the 34th International Conference on Machine Learning, 70, 214-223.
61.Kong, H. & Kim, W. (2019). Generating summary sentences using Adversarially Regularized Autoencoders with conditional context. ScienceDirect, 130, 1-11. doi: 10.1016/j.eswa.2019.04.014
62.Morris, G.M. & Lim-Wilby, M. (2008). Molecular docking. Methods Mol Biol, 443, 365-382. doi: 10.1007/978-1-59745-177-2_19
63.Berman, H.M. & Westbrook, J. & Feng, Z. & Gilliland, G. & Bhat, T.N. & Weissig, H. & Shindyalov, I.N. & Bourne, P. E. (2000). The Protein Data Bank. Nucleic Acids Research, 28 (1), 235-242. doi: 10.1093/nar/28.1.235
64.Hu, S.H. & Li, F. Y. (2014). Modeling of human ghrelin receptor and simulation of its ligand-binding modes. Unpublished master dissertation, Institute of Chemistry, National Chung Hsing University, Taichung City.
65.Trott, O. & Olson, A.J. (2009). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31 (2), 455-461. doi: 10.1002/jcc.2133
66.Chen, X. & Lin, Y. & Gilson, M. K. (2002). The binding database: Overview and user's guide. Biopolymers, 61 (2), 127-141. doi: 10.1002/1097-0282(2002)61:2<127::AID-BIP10076>3.0.CO;2-N
67.Abadi, M. & Barham, P. & Chen, J. & Chen, Z. & Davis, A. & Dean, J. & Devin, M. & Ghemawat, S. & Irving, G. & Isard, M. & Kudlur, M. & Levenberg, J. & Monga, R. & Moore, S. & Murray, D. G. & Steiner, B. & Tucker, P. & Vasudevan, V. & Warden, P. & Wicke, M. & Yu, Y. & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning, Google Brain, 265-283. arXiv: 1605.08695v2
68.Gulli, A. & Pal, S. (2017). Deep learning with Keras. Packt Publishing Ltd
69.Duchi, J. & Hazan, E. & Singer, Y. (2011). Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. JMLR, 12 (61), 2121-2159.
70.Nair, V. & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning, 807-814.
71.Dearden, J. C. & Cronin, M. T. D. & Kaiser, K. L. E. (2009). How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR and QSAR in Environmental Research, 20 (3-4), 241-266. doi: 10.1080/10629360902949567
72.Ke, Y. Y. & Lin, T. H. (2006). Modeling the Ligand−Receptor Interaction for a Series of Inhibitors of the Capsid Protein of Enterovirus 71 Using Several Three-Dimensional Quantitative Structure−Activity Relationship Techniques. Journal of Medicinal Chemistry, 49 (15), 4517-4525.
73.Cherkasov, A. & Muratov, E. N. & Fourches, D. & Varnek, A. & Baskin, I. I. & Cronin, M. & Dearden, J. & Gramatica, P. & Martin, Y. C. & Todeschini, R. & Consonni, V. & Kuz’min, V. E. & Cramer, R. & Benigni, R. & Yang, C. & Rathman, J. & Terfloth, L. & Gasteiger, J. & Richard, A. & Tropsha, A. (2014). QSAR Modeling: Where Have You Been? Where Are You Going To?. Journal of Medicinal Chemistry, 57 (12), 4977-5010. doi: 10.1021/jm4004285
74.Pedregosa, F. & Varoquaux, G. & Gramfort, A. & Michel, V. & Thirion, B. & Grisel, O. & Blondel, M. & Prettenhofer, P. & Weiss, R. & Dubourg, V. & Vanderplas, J. & Passos, A. & Cournapeau, D. & Brucher, M. & Perrot, M. & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. JMLR, 12 (85), 2825-2830.
75.LI, J. G. & Chen, C. & Yin, J. & Rice, K. & Zhang, Y. & Matecka, D. & Kimde Riel, J. & Desjarlais, R. L. & Yuan, L. & Chen, L. (1999). Asp147 in the third transmembrane helix of the rat μ opioid receptor forms ion-pairing with morphine and naltrexone. ScienceDirect, 65 (2), 175-185. doi: 10.1016/S0024-3205(99)00234-9
76.Humberto, G. D. & Bonet, I. & Terán, C. & Clercq, E. D. & Bello, R. & García, M. M. & Santana, L. & Uriarte, E. (2007). ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. European Journal of Medicinal Chemistry, 42 (5), 580-585. doi: 10.1016/j.ejmech.2006.11.016
77.He, R. & Ma, H. & Zhao, W. & Qu, W. & Zhao, J. & Luo, L. & Zhu, W. (2011). Modeling the QSAR of ACE-Inhibitory Peptides with ANN and Its Applied Illustration. International Journal of Peptides, 2012, 9 doi: 10.1155/2012/620609
78.Tsou, L. K. & Yeh, S. H. & Ueng, S. H. & Chang, C. P. & Song, J. S. & Wu, M. H. & Chang, H. F. & Chen, S. R. & Shih, C. & Chen, C. T. & Ke, Y. Y. (2020). Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci Rep, 10 (1), 16771 doi: 10.1038/s41598-020-73681-1
79.Velázquez-Libera, J. L. & Durán-Verdugo, F. & Valdés-Jiménez, A. & Núñez-Vivanco, G. & Caballero, J. (2020). LigRMSD: a web server for automatic structure matching and RMSD calculations among identical and similar compounds in protein-ligand docking. Bioinformatics, 36 (9), 2912-2914. doi: 10.1093/bioinformatics/btaa018
80.Cao, Y. & Jiang, T. & Girke, T. (2008). A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Bioinformatics, 24 (13), i366-74. doi: 10.1093/bioinformatics/btn186
81.Langevin, M. & Minoux, H. & Levesque, M. & Bianciotto, M. (2020). Scaffold-Constrained Molecular Generation. American Chemical Society, 60 (12), 5637-5646. doi: 10.1021/acs.jcim.0c01015
82.Custodio, J. M. & Wu, C. Y. & Benet, L. Z. (2008). Predicting drug disposition, absorption/elimination/transporter interplay and the role of food on drug absorption. Adv Drug Deliv Rev, 60 (6), 717-733. doi: 10.1016/j.addr.2007.08.043
83.Siuda, E. R. & 3rd, R. C. & Rominger, D. H. & Violin, J. D. (2017). Biased mu-opioid receptor ligands: a promising new generation of pain therapeutics. Curr Opin Pharmacol, 32, 77-84. doi: 10.1016/j.coph.2016.11.007
84.Santoro, D. & Bellinghieri, G. & Savica, V. (2011). Development of the concept of pain in history. J Nephrol, 17, S133-6. doi: 10.5301/JN.2011.6481
85.Stanley, T. H. (2014). The fentanyl story. J Pain, 15 (12), 1215-1226. doi: 10.1016/j.jpain.2014.08.010
86.Kudla, L. & Bugno, R. & Skupio, U. & Wiktorowska, L. & Solecki, W. & Wojtas, A. & Golembiowska , K. & Zádor, F. & Benyhe, S. & Buda, S. & Makuch, W. & Przewlocka, B. & Bojarski , A. J. & Przewlocki, R. (2019). Functional characterization of a novel opioid, PZM21, and its effects on the behavioural responses to morphine. Br J Pharmacol, 176 (23), 4434-4445. doi: 10.1111/bph.14805
87.Yamamoto, L.T. & Horie, S. & Takayama, H. & Aimi, N. & Sakai, S. & Yano, S. & Shan, J. & Pang, P. K. & Ponglux, D. & Watanabe, K. (1999). Opioid receptor agonistic characteristics of mitragynine pseudoindoxyl in comparison with mitragynine derived from Thai medicinal plant Mitragyna speciosa. Gen Pharmacol, 33 (1), 73-81. doi: 10.1016/s0306-3623(98)00265-1
88.Carney, T. & Van Hout, M. C. & Norman, I. & Dada, S. & Siegfried, N. & Parry, C. D. (2020). Dihydrocodeine for detoxification and maintenance treatment in individuals with opiate use disorders. Cochrane Database Syst Rev, 2 (2), CD012254. doi: 10.1002/14651858.CD012254.pub2
89.Leppert,W. (2010). Dihydrocodeine as an opioid analgesic for the treatment of moderate to severe chronic pain. Curr Drug Metab, 11 (6), 494-506. doi: 10.2174/138920010791636211
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
1. 利用深度學習對人類二氫葉酸還原酶抑制劑進行分子建模研究
2. 利用異丙醇鹽析法分離釀造天門冬酒中活性成分化合物
3. 藉由鈀金屬催化的碳-氫鍵活化機制應用於雙苯醌與三級胺反應及將降冰片烯插入吲哚-4,9-二酮衍生物之研究
4. 轉移法製備軟性表面訊號增益拉曼影像感測基板 的效果探討
5. 含喹唑啉酮配位基之鋁金屬催化劑的合成、鑑定以及對ε-環己內酯開環聚合反應之應用
6. 利用金奈米粒子/二氧化鈦奈米線之光電化學免疫感測器偵測血清中甲型胎兒蛋白
7. 三苯胺取代鐵紫質之電聚合對二氧化碳還原催化反應
8. 1.含銅金屬有機框架HKUST-1催化芳香基碘化物與硫醇交互耦合反應之應用2.碘催化有機電化學反應合成次磺醯胺化合物
9. 性固醇類內泌素於牛子宮內膜上皮細胞對生長因子表現之調控
10. 建立小鼠卵巢體外培養之最適條件
11. (1)在無金屬,無氧化劑及無溶劑的條件下利用六甲基二矽氮烷來有效合成磺醯胺脒(2)利用磺醯胺脒與胺類於水相有效合成甲醯胺
12. 含喹唑啉衍生之類脒基鋁金屬錯合物的合成、鑑定以及對環己內酯開環聚合反應之應用
13. 硼酸修飾金塗覆鋁晶片結合基質輔助雷射脫附游離飛行時間質譜術應用於醣蛋白之純化及鑑定
14. 電化學探討單寧酸分子在碳基材上的吸附現象
15. 1.利用鹼性氧化銅材料催化芳香基及烷基碘化物與二硒化物進行交互耦合反應2.藍色發光二極體促進芳香二硫/硒化物進行磷酸化合成硫/硒代磷酸酯