In silico methodologies for selection and prioritization of compounds in drug discovery

IN SILICO METHODOLOGIES FOR SELECTION AND PRIORITIZATION OF COMPOUNDS IN DRUG DISCOVERY YEO WEE KIANG (M.Sc. (Bioinformatics), NTU) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF PHARMACY NATIONAL UNIVERSITY OF SINGAPORE 2012 DECLARATION I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. Yeo Wee Kiang 10th September 2012 i ACKNOWLEDGEMENTS It is a great pleasure to acknowledge the support that I have received during my doctoral research. First, I must express my heartfelt gratitude to my academic supervisor at the National University of Singapore, Associate Professor Go Mei Lin for her patience, guidance and the opportunity to be part of her research group. Her receptiveness to novel ideas and her research experience has provided me both the freedom to explore as well as the delicate environment where new ideas can be incubated without premature reprisal. In spite of her many commitments, she has always been approachable and generous with her time. From time to time, I wonder how she sustains her constantly high energy levels and neverending enthusiasm. She is a ready role model for how an Investigator and mentor should be. Indeed, it is my good fortune to have Prof Go as my academic supervisor. My sincere appreciation also goes to Dr Shahul Nilar, my industry supervisor at the Novartis Institute for Tropical Diseases (NITD) Computational Chemistry team, for imbuing me with copious amounts of optimism amidst the trials and tribulations of industrial drug discovery. His continuous encouragement, critique and guidance have been instrumental to my work. Most importantly, he has inculcated in me the value of healthy scepticism and imparted the ‘thinking’ approach to conducting innovative research. Dr Nilar achieved that by providing abundant ‘space’ for me to tinker with alternative methods to solve problems instead of merely shoving down a dogmatic solution. ii It was during my days as a graduate student that I experienced the unbelievable power of conceptual combination and morphological analysis. Hence I am now able to appreciate their contributions to problem-solving and their roles in innovation. Indeed, I am grateful that both of my supervisors have given me the opportunity to experience the joy and exhilaration of scientific discovery. I would also like to thank Dr Thomas Keller, former Head of Chemistry Unit at NITD for his guidance and the opportunity to work in the lively community of more than 100 international researchers at NITD. I am grateful to Dr Paul Smith, Head of Chemistry at NITD, for providing critical suggestions that sharpened my work. Also, I would like to thank Dr Ida Ma for providing expertise critique of my projects and the corresponding manuscripts. Next, I am indebted to Dr Lim Siew Pheng and Dr Chen Yen-Liang and their teams at the NITD Disease Biology Unit for performing the Dengue RNA dependent RNA polymerase assays and for sharing their knowledge on the enzyme. I would also like to thank Dr David Beer and his team, NITD Screening Unit, who conducted the primary and reconfirmation screens that I have used for the compound selection and prioritization aspect of my research work. My sincere gratitude also goes to Mr Koh Siang Boon and Ms Meg Tan Kheng Lin who put in enormous effort to synthesize the compounds for the Taguchi method section of my research work. In particular, they conducted the corresponding biological assays that were instrumental to the validation of the method. iii I am also grateful to my friends, colleagues, lab-mates and fellow graduate students (some of whom have since graduated):  Ms Meera Gurumurthy, Ms Pramila Ghode, Ms Michelle Lim, Ms Pearly Ng, Ms Gladys Lee, Mr Ian Heng and Ms Aznilah Lathiff from NITD;  Dr Jenefer Alam and Ms Ngew Xinyi formerly from NITD;  Dr Low Kai Leng formerly from Department of Biochemistry, NUS;  Dr Zhang Wei, Dr Leow Jo Lene, Dr Lee Chong Yew, Dr Sim Hong May, Dr Nguyen Thi Hanh Thuy, Dr Wee Xi Kai, Mr Pondy Murgappan Ramanujulu, Ms Chen Xiao, Ms Meg Tan Kheng Lin, Ms Xu Jin, Mr Sherman Ho, Ms Sim Mei Yi, Ms Yap Siew Qi, Dr Suresh Kumar Gorla and Dr Yang Tianming, from Assoc Prof Go’s lab group in the Department of Pharmacy, NUS; The PhD scholarship from NITD is hereby gratefully acknowledged. Besides financial support for my tuition fees, it has funded me generously to attend international conferences that provided the precious opportunities to meet and interact with eminent colleagues abroad. Without such big-hearted support, international conferences would have been out of reach for graduate students like me. In all, Novartis has offered me exceptional opportunities for realworld insights into the science, technology and highly collaborative nature of modern drug discovery in the pharmaceutical industry. iv PUBLICATIONS & CONFERENCES This thesis is based on the following papers (listed in chronological order of the date of publication), manuscripts and other unpublished data: Publications 1. Wee Kiang Yeo, Kheng Lin Tan, Siang Boon Koh, Matiullah Khan, Shahul H. Nilar and Mei Lin Go. Exploration and Optimization of Structure–Activity Relationships in Drug Design using the Taguchi Method. ChemMedChem, 2012, 7, 977-982. 2. Wee Kiang Yeo, Mei Lin Go and Shahul H. Nilar. Extraction and validation of substructure profiles for enriching compound libraries. Journal of Computer-Aided Molecular Design, 2012, accepted for publication. Manuscripts in preparation 1. Wee Kiang Yeo, Thomas H. Keller, Mei Lin Go and Shahul H. Nilar. A novel approach to compound selection and prioritization for hits from High-Throughput Screening campaigns. Manuscript in preparation. 2. Wee Kiang Yeo, Chin Chin Lim, Feng Gu, Yen-Liang Chen, Siew Pheng Lim, Mei Lin Go and Shahul H. Nilar. Multistep virtual screening for identification of non-nucleoside inhibitors of dengue RNA-dependent RNA polymerase. Manuscript in preparation. The following papers were published in the course of the Ph.D. study but not form part of this thesis: v 1. Xi Kai Wee, Wee Kiang Yeo, Bing Zhang, Vincent B.C. Tan, Kian Meng Lim, Tong Earn Tay and Mei Lin Go. Synthesis and evaluation of functionalized isoindigos as antiproliferative agents. Bioorganic & Medicinal Chemistry, 2009, 17, 7562-7571. 2. Kai Leng Low, Guanghou Shui, Klaus Natter, Wee Kiang Yeo, Sepp D. Kohlwein, Thomas Dick, P.S. Srinivasa Rao and Markus R. Wenk. Lipid droplet-associated proteins are involved in the biosynthesis and hydrolysis of triacylglycerol in Mycobacterium bovis Bacillus Calmette-Guérin. Journal of Biological Chemistry, 2010, 285, 21662-21670. 3. Hong May Sim, Ker Yun Loh, Wee Kiang Yeo, Chong Yew Lee and Mei Lin Go. Aurones as modulators of ABCG2 and ABCB1: Synthesis and Structure-activity relationships. ChemMedChem, 2011, 6, 713-724. CONFERENCE PRESENTATIONS (ORAL) 1. 11th Asia Pacific Rim Universities (APRU) Doctoral Students Conference (12th to 16th July, 2010, Jakarta, Indonesia): Research for the Sustainability of Civilization in Pacific Rim: Past, Present and Future. Oral presentation title: “Expediting the lead optimization phase of drug discovery using ‘Design of Experiments’ methods”. 2. 6th American Association of Pharmaceutical Scientists-National University of Singapore (AAPS-NUS) Student Chapter Scientific Symposium (7th April 2010, Singapore). Oral presentation title: “A novel approach to compound selection and prioritization for hits from High-Throughput Screening campaigns”. vi CONFERENCE PRESENTATIONS (POSTER) 1. 7th American Association of Pharmaceutical Scientists-National University of Singapore (AAPS-NUS) Student Chapter Pharmsci@Asia Symposium (6th June 2012, Singapore): Exploring Pharmaceutical Sciences: New Challenges & Opportunities. Poster title: “Extraction and validation of substructure profiles for enriching compound libraries”. 2. Annual National University of Singapore Pharmacy Symposium 2012 (4th April 2012, Singapore). Poster title: “Exploration and Optimization of Structure–Activity Relationships in Drug Design using the Taguchi Method”. 3. Gordon Research Conference on Computer-Aided Drug Design 2011 (17th – 22nd July 2011, Mount Snow Resort, West Dover, Vermont, United States of America). Poster title: “A Random Forest Clustering Approach to Compound Selection and Prioritization for High-Throughput Screening Campaigns”. 4. The 7th International Symposium for Chinese Medicinal Chemists (1st-5th February 2010, Kaohsiung, Republic of China). Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. 5. UK-Singapore Symposium on Medicinal Chemistry 2010 (25th – 26th January 2010, Biopolis, Singapore). vii Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. 6. Molecular Modelling 2009: Molecular Modelling from Dynamical, Bio-molecular and Materials Nanotechnology Perspectives (26th-29th July 2009, Gold Coast, Australia). Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. viii TABLE OF CONTENTS DECLARATION i ACKNOWLEDGEMENTS .ii PUBLICATIONS & CONFERENCES . v Conference presentations (Oral) . vi Conference presentations (Poster) .vii TABLE OF CONTENTS ix SUMMARY xi LIST OF TABLES . xiii LIST OF FIGURES .xvii LIST OF ABBREVIATIONS xx CHAPTER DISCOVERY INTRODUCTION TO COMPUTATIONAL METHODS IN DRUG 1.1 Introduction 1.2 Virtual Screening . 1.3 Molecular Docking & Scoring Functions 1.4 Molecular Similarity 1.5 Pharmacophores . 1.6 Substructure Searching 1.7 Machine Learning in Virtual Screening . 11 1.8 Statement of Purpose . 13 CHAPTER HIGH THROUGHPUT SCREENING HIT LIST TRIAGING . 16 2.1 Introduction 16 2.2 Materials and Methods . 23 2.2.1 Datasets 23 2.2.2 Pre-processing 24 2.2.3 Decision Stump 25 2.2.4 Random Forest Clustering . 26 2.2.5 Descriptor Selection . 27 2.3 Results and Discussion 31 2.3.1 Performance of Random Forest Clustering, Decision Stump versus µ+3σ Method using 14 descriptors . 31 2.3.2 Performance of Random Forest Clustering using Hopkins-based selected descriptors versus 14 descriptors . 42 ix BIBLIOGRAPHY 217. Chen, T.; George, J. A.; Taylor, C. C. Src tyrosine kinase as a chemotherapeutic target: is there a clinical case? Anti-cancer drugs 2006, 17, 123-31. 218. Heron-Milhavet, L.; Khouya, N.; Fernandez, A.; Lamb, N. J. Akt1 and Akt2: differentiating the aktion. Histology and histopathology 2011, 26, 651-62. 219. Kawakami, T.; Kawakami, Y.; Kitaura, J. Protein kinase C beta (PKC beta): normal functions and diseases. J Biochem 2002, 132, 677-82. 220. Yung-Chi, C.; Prusoff, W. H. Relationship between the inhibition constant (KI) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochemical Pharmacology 1973, 22, 3099-3108. 221. Liew, C. Y.; Ma, X. H.; Yap, C. W. Consensus model for identification of novel PI3K inhibitors in large chemical library. Journal of Computer-Aided Molecular Design 2010, 24, 131-141. 222. Yap, C. W. PaDEL-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. Journal of Computational Chemistry 2011, 32, 1466-1474. 223. Li, Q. L.; Chen, T. J.; Wang, Y. L.; Bryant, S. H. PubChem as a public resource for drug discovery. DRUG DISCOVERY TODAY 2010, 15, 1052-1057. 224. Bryant, S. PubChem: An information resource linking chemistry and biology. Abstr Pap Am Chem S 2006, 231. 225. PubChem Fingerprints. ftp://ftp.ncbi.nih.gov/pubchem/data_spec/pubchem_fingerprints.txt 226. Klekota, J.; Roth, F. P. Chemical substructures that enrich for biological activity. Bioinformatics 2008, 24, 2518-2525. 227. Japkowicz, N.; Shah, M. Performance measures I. In Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press: 2011. 228. Chatterjee, A. K.; Yeung, B. K. Back to the Future: Lessons Learned in Modern Target-based and Whole-Cell Lead Optimization of Antimalarials. Curr Top Med Chem 2012, 12, 473-83. 229. Kortagere, S.; Lill, M.; Kerrigan, J. Role of Computational Methods in Pharmaceutical Sciences. In Computational Toxicology, Reisfeld, B.; Mayeno, A. N., Eds. Humana Press: 2012; Vol. 929, pp 21-48. 230. Neglected Tropical Diseases, Hidden successes, Emerging opportunities. In World Health Organization: 2006. 231. Mackey, T. K.; Liang, B. A. Threats from emerging and re-emerging neglected tropical diseases (NTDs). Infection ecology & epidemiology 2012, 2. 232. Trouiller, P.; Olliaro, P.; Torreele, E.; Orbinski, J.; Laing, R.; Ford, N. Drug development for neglected diseases: a deficient market and a public-health policy failure. The Lancet 2002, 359, 2188-2194. 233. Gubler, D. J. The global emergence/resurgence of arboviral diseases as public health problems. Arch Med Res 2002, 33, 330-42. 234. Bartenschlager, R.; Miller, S. Molecular aspects of Dengue virus replication. Future Microbiology 2008, 3, 155-165. 235. Cramer, P.; Arnold, E. Proteins: how RNA polymerases work. Curr Opin Struct Biol 2009, 19, 680-2. 236. Leveque, V. J.; Wang, Q. M. RNA-dependent RNA polymerase encoded by hepatitis C virus: biomedical applications. Cell Mol Life Sci 2002, 59, 909-19. 237. Malet, H.; Masse, N.; Selisko, B.; Romette, J. L.; Alvarez, K.; Guillemot, J. C.; Tolou, H.; Yap, T. L.; Vasudevan, S.; Lescar, J.; Canard, B. The flavivirus polymerase as a target for drug discovery. Antivir Res 2008, 80, 23-35. 238. Neyts, J. Selective inhibitors of hepatitis C virus replication. Antiviral Res 2006, 71, 363-71. 149 BIBLIOGRAPHY 239. Paula, T.; Pablo, R.; Eugenia, V.; Pablo, B.; Sabino, P.; Jose, M.; Antonio, M.; Dolores, H. M.; Pablo, L.; Javier, G. S.; Vincente, S. New drug targets for hepatitis C and other Flaviviridae viruses. Infect Disord Drug Targets 2009, 9, 133-47. 240. Rawlinson, S. M.; Pryor, M. J.; Wright, P. J.; Jans, D. A. Dengue virus RNA polymerase NS5: a potential therapeutic target? Curr Drug Targets 2006, 7, 1623-38. 241. Sampath, A.; Padmanabhan, R. Molecular targets for flavivirus drug discovery. Antivir Res 2009, 81, 6-15. 242. Walker, M. P.; Hong, Z. HCV RNA-dependent RNA polymerase as a target for antiviral development. Curr Opin Pharmacol 2002, 2, 534-40. 243. Wu, J. Z.; Hong, Z. Targeting NS5B RNA-dependent RNA polymerase for anti-HCV chemotherapy. Curr Drug Targets Infect Disord 2003, 3, 207-19. 244. Ahmed-Belkacem, A.; Ahnou, N.; Barbotte, L.; Wychowski, C.; Pallier, C.; Brillet, R.; Pohl, R. T.; Pawlotsky, J. M. Silibinin and related compounds are direct inhibitors of hepatitis C virus RNA-dependent RNA polymerase. Gastroenterology 2010, 138, 1112-22. 245. Angusti, A.; Manfredini, S.; Durini, E.; Ciliberti, N.; Vertuani, S.; Solaroli, N.; Pricl, S.; Ferrone, M.; Fermeglia, M.; Loddo, R.; Secci, B.; Visioli, A.; Sanna, T.; Collu, G.; Pezzullo, M.; La Colla, P. Design, synthesis and anti flaviviridae activity of N(6)-, 5',3'-Oand 5',2'-O-substituted adenine nucleoside analogs. Chem Pharm Bull (Tokyo) 2008, 56, 42332. 246. Beaulieu, P. L. Non-nucleoside inhibitors of the HCV NS5B polymerase: progress in the discovery and development of novel agents for the treatment of HCV infections. Curr Opin Investig Drugs 2007, 8, 614-34. 247. Beaulieu, P. L. Finger loop inhibitors of the HCV NS5B polymerase: discovery and prospects for new HCV therapy. Curr Opin Drug Discov Devel 2006, 9, 618-26. 248. Beaulieu, P. L.; Tsantrizos, Y. S. Inhibitors of the HCV NS5B polymerase: new hope for the treatment of hepatitis C infections. Curr Opin Investig Drugs 2004, 5, 838-50. 249. Betzi, S.; Eydoux, C.; Bussetta, C.; Blemont, M.; Leyssen, P.; Debarnot, C.; BenRahou, M.; Haiech, J.; Hibert, M.; Gueritte, F.; Grierson, D. S.; Romette, J. L.; Guillemot, J. C.; Neyts, J.; Alvarez, K.; Morelli, X.; Dutartre, H.; Canard, B. Identification of allosteric inhibitors blocking the hepatitis C virus polymerase NS5B in the RNA synthesis initiation step. Antiviral Res 2009, 84, 48-59. 250. Biswal, B. K.; Wang, M.; Cherney, M. M.; Chan, L.; Yannopoulos, C. G.; Bilimoria, D.; Bedard, J.; James, M. N. Non-nucleoside inhibitors binding to hepatitis C virus NS5B polymerase reveal a novel mechanism of inhibition. J Mol Biol 2006, 361, 33-45. 251. Burton, G.; Ku, T. W.; Carr, T. J.; Kiesow, T.; Sarisky, R. T.; Lin-Goerke, J.; Baker, A.; Earnshaw, D. L.; Hofmann, G. A.; Keenan, R. M.; Dhanak, D. Identification of small molecule inhibitors of the hepatitis C virus RNA-dependent RNA polymerase from a pyrrolidine combinatorial mixture. Bioorg Med Chem Lett 2005, 15, 1553-6. 252. Burton, G.; Ku, T. W.; Carr, T. J.; Kiesow, T.; Sarisky, R. T.; Lin-Goerke, J.; Hofmann, G. A.; Slater, M. J.; Haigh, D.; Dhanak, D.; Johnson, V. K.; Parry, N. R.; Thommes, P. Studies on acyl pyrrolidine inhibitors of HCV RNA-dependent RNA polymerase to identify a molecule with replicon antiviral activity. Bioorg Med Chem Lett 2007, 17, 1930-3. 253. Carroll, S. S.; Ludmerer, S.; Handt, L.; Koeplinger, K.; Zhang, N. R.; Graham, D.; Davies, M. E.; MacCoss, M.; Hazuda, D.; Olsen, D. B. Robust antiviral efficacy upon administration of a nucleoside analog to hepatitis C virus-infected chimpanzees. Antimicrob Agents Chemother 2009, 53, 926-34. 254. Carroll, S. S.; Olsen, D. B. Nucleoside analog inhibitors of hepatitis C virus replication. Infect Disord Drug Targets 2006, 6, 17-29. 150 BIBLIOGRAPHY 255. Carroll, S. S.; Tomassini, J. E.; Bosserman, M.; Getty, K.; Stahlhut, M. W.; Eldrup, A. B.; Bhat, B.; Hall, D.; Simcoe, A. L.; LaFemina, R.; Rutkowski, C. A.; Wolanski, B.; Yang, Z.; Migliaccio, G.; De Francesco, R.; Kuo, L. C.; MacCoss, M.; Olsen, D. B. Inhibition of hepatitis C virus RNA replication by 2'-modified nucleoside analogs. J Biol Chem 2003, 278, 11979-84. 256. Chan, L.; Reddy, T. J.; Proulx, M.; Das, S. K.; Pereira, O.; Wang, W.; Siddiqui, A.; Yannopoulos, C. G.; Poisson, C.; Turcotte, N.; Drouin, A.; Alaoui-Ismaili, M. H.; Bethell, R.; Hamel, M.; L'Heureux, L.; Bilimoria, D.; Nguyen-Ba, N. Identification of N,N-disubstituted phenylalanines as a novel class of inhibitors of hepatitis C NS5B polymerase. J Med Chem 2003, 46, 1283-5. 257. Cheek, M. A.; Dobrikov, M. I.; Wennefors, C. K.; Xu, Z.; Hashmi, S. N.; Shen, X.; Shaw, B. R. Synthesis and properties of (alpha-P-borano)-nucleoside 5'-triphosphate analogues as potential antiviral agents. Nucleic Acids Symp Ser (Oxf) 2008, 81-2. 258. Chen, C. M.; He, Y.; Lu, L.; Lim, H. B.; Tripathi, R. L.; Middleton, T.; Hernandez, L. E.; Beno, D. W.; Long, M. A.; Kati, W. M.; Bosse, T. D.; Larson, D. P.; Wagner, R.; Lanford, R. E.; Kohlbrenner, W. E.; Kempf, D. J.; Pilot-Matias, T. J.; Molla, A. Activity of a potent hepatitis C virus polymerase inhibitor in the chimpanzee model. Antimicrob Agents Chemother 2007, 51, 4290-6. 259. Clark, J. L.; Hollecker, L.; Mason, J. C.; Stuyver, L. J.; Tharnish, P. M.; Lostia, S.; McBrayer, T. R.; Schinazi, R. F.; Watanabe, K. A.; Otto, M. J.; Furman, P. A.; Stec, W. J.; Patterson, S. E.; Pankiewicz, K. W. Design, synthesis, and antiviral activity of 2'-deoxy-2'fluoro-2'-C-methylcytidine, a potent inhibitor of hepatitis C virus replication. J Med Chem 2005, 48, 5504-8. 260. D'Abramo, C. M.; Cellai, L.; Gotte, M. Excision of incorporated nucleotide analogue chain-terminators can diminish their inhibitory effects on viral RNA-dependent RNA polymerases. J Mol Biol 2004, 337, 1-14. 261. De Francesco, R.; Tomei, L.; Altamura, S.; Summa, V.; Migliaccio, G. Approaching a new era for hepatitis C virus therapy: inhibitors of the NS3-4A serine protease and the NS5B RNA-dependent RNA polymerase. Antiviral Res 2003, 58, 1-16. 262. Del Vecchio, A. M.; Sarisky, R. T. Small molecule and biologic inhibitors of hepatitis C virus: a symbiotic approach. Mini Rev Med Chem 2006, 6, 1263-8. 263. Deng, Y.; Shipps, G. W., Jr.; Wang, T.; Popovici-Muller, J.; Rosner, K. E.; Siddiqui, M. A.; Duca, J.; Cooper, A. B.; Cable, M. Discovery of 4H-pyrazolo[1,5-a]pyrimidin-7-ones as potent inhibitors of hepatitis C virus polymerase. Bioorg Med Chem Lett 2009, 19, 5363-7. 264. Deval, J.; Powdrill, M. H.; D'Abramo, C. M.; Cellai, L.; Gotte, M. Pyrophosphorolytic excision of nonobligate chain terminators by hepatitis C virus NS5B polymerase. Antimicrob Agents Chemother 2007, 51, 2920-8. 265. Dhanak, D.; Duffy, K. J.; Johnston, V. K.; Lin-Goerke, J.; Darcy, M.; Shaw, A. N.; Gu, B.; Silverman, C.; Gates, A. T.; Nonnemacher, M. R.; Earnshaw, D. L.; Casper, D. J.; Kaura, A.; Baker, A.; Greenwood, C.; Gutshall, L. L.; Maley, D.; DelVecchio, A.; Macarron, R.; Hofmann, G. A.; Alnoah, Z.; Cheng, H. Y.; Chan, G.; Khandekar, S.; Keenan, R. M.; Sarisky, R. T. Identification and biological characterization of heterocyclic inhibitors of the hepatitis C virus RNA-dependent RNA polymerase. J Biol Chem 2002, 277, 38322-7. 266. Di Santo, R.; Fermeglia, M.; Ferrone, M.; Paneni, M. S.; Costi, R.; Artico, M.; Roux, A.; Gabriele, M.; Tardif, K. D.; Siddiqui, A.; Pricl, S. Simple but highly effective threedimensional chemical-feature-based pharmacophore model for diketo acid derivatives as hepatitis C virus RNA-dependent RNA polymerase inhibitors. J Med Chem 2005, 48, 630414. 151 BIBLIOGRAPHY 267. Ding, Y.; Girardet, J. L.; Smith, K. L.; Larson, G.; Prigaro, B.; Lai, V. C.; Zhong, W.; Wu, J. Z. Parallel synthesis of pteridine derivatives as potent inhibitors for hepatitis C virus NS5B RNA-dependent RNA polymerase. Bioorg Med Chem Lett 2005, 15, 675-8. 268. Ding, Y.; Girardet, J. L.; Smith, K. L.; Larson, G.; Prigaro, B.; Wu, J. Z.; Yao, N. Parallel synthesis of 5-cyano-6-aryl-2-thiouracil derivatives as inhibitors for hepatitis C viral NS5B RNA-dependent RNA polymerase. Bioorg Chem 2006, 34, 26-38. 269. Donner, P. L.; Xie, Q.; Pratt, J. K.; Maring, C. J.; Kati, W.; Jiang, W.; Liu, Y.; Koev, G.; Masse, S.; Montgomery, D.; Molla, A.; Kempf, D. J. Des-A-ring benzothiadiazines: inhibitors of HCV genotype NS5B RNA-dependent RNA polymerase. Bioorg Med Chem Lett 2008, 18, 2735-8. 270. Dragovich, P. S.; Blazel, J. K.; Ellis, D. A.; Han, Q.; Kamran, R.; Kissinger, C. R.; LeBrun, L. A.; Li, L. S.; Murphy, D. E.; Noble, M.; Patel, R. A.; Ruebsam, F.; Sergeeva, M. V.; Shah, A. M.; Showalter, R. E.; Tran, C. V.; Tsan, M.; Webber, S. E.; Kirkovsky, L.; Zhou, Y. Novel HCV NS5B polymerase inhibitors derived from 4-(1',1'-dioxo-1',4'-dihydro1'lambda(6)-benzo[1',2',4']thiadiazin-3'-yl)- 5-hydroxy-2H-pyridazin-3-ones. Part 5: Exploration of pyridazinones containing 6-amino-substituents. Bioorg Med Chem Lett 2008, 18, 5635-9. 271. Eldrup, A. B.; Allerson, C. R.; Bennett, C. F.; Bera, S.; Bhat, B.; Bhat, N.; Bosserman, M. R.; Brooks, J.; Burlein, C.; Carroll, S. S.; Cook, P. D.; Getty, K. L.; MacCoss, M.; McMasters, D. R.; Olsen, D. B.; Prakash, T. P.; Prhavc, M.; Song, Q.; Tomassini, J. E.; Xia, J. Structure-activity relationship of purine ribonucleosides for inhibition of hepatitis C virus RNA-dependent RNA polymerase. J Med Chem 2004, 47, 2283-95. 272. Ellis, D. A.; Blazel, J. K.; Tran, C. V.; Ruebsam, F.; Murphy, D. E.; Li, L. S.; Zhao, J.; Zhou, Y.; McGuire, H. M.; Xiang, A. X.; Webber, S. E.; Zhao, Q.; Han, Q.; Kissinger, C. R.; Lardy, M.; Gobbi, A.; Showalter, R. E.; Shah, A. M.; Tsan, M.; Patel, R. A.; LeBrun, L. A.; Kamran, R.; Bartkowski, D. M.; Nolan, T. G.; Norris, D. A.; Sergeeva, M. V.; Kirkovsky, L. 5,5'- and 6,6'-dialkyl-5,6-dihydro-1H-pyridin-2-ones as potent inhibitors of HCV NS5B polymerase. Bioorg Med Chem Lett 2009, 19, 6047-52. 273. Flisiak, R.; Dumont, J. M.; Crabbe, R. Cyclophilin inhibitors in hepatitis C viral infection. Expert Opin Investig Drugs 2007, 16, 1345-54. 274. Giuliano, C.; Fiore, F.; Di Marco, A.; Padron Velazquez, J.; Bishop, A.; Bonelli, F.; Gonzalez-Paz, O.; Marcucci, I.; Harper, S.; Narjes, F.; Pacini, B.; Monteagudo, E.; Migliaccio, G.; Rowley, M.; Laufer, R. Preclinical pharmacokinetics and metabolism of a potent non-nucleoside inhibitor of the hepatitis C virus NS5B polymerase. Xenobiotica 2005, 35, 1035-54. 275. Gopalsamy, A.; Aplasca, A.; Ciszewski, G.; Park, K.; Ellingboe, J. W.; Orlowski, M.; Feld, B.; Howe, A. Y. Design and synthesis of 3,4-dihydro-1H-[1]-benzothieno[2,3-c]pyran and 3,4-dihydro-1H-pyrano[3,4-b]benzofuran derivatives as non-nucleoside inhibitors of HCV NS5B RNA dependent RNA polymerase. Bioorg Med Chem Lett 2006, 16, 457-60. 276. Gopalsamy, A.; Chopra, R.; Lim, K.; Ciszewski, G.; Shi, M.; Curran, K. J.; Sukits, S. F.; Svenson, K.; Bard, J.; Ellingboe, J. W.; Agarwal, A.; Krishnamurthy, G.; Howe, A. Y.; Orlowski, M.; Feld, B.; O'Connell, J.; Mansour, T. S. Discovery of proline sulfonamides as potent and selective hepatitis C virus NS5b polymerase inhibitors. Evidence for a new NS5b polymerase binding site. J Med Chem 2006, 49, 3052-5. 277. Gopalsamy, A.; Lim, K.; Ciszewski, G.; Park, K.; Ellingboe, J. W.; Bloom, J.; Insaf, S.; Upeslacis, J.; Mansour, T. S.; Krishnamurthy, G.; Damarla, M.; Pyatski, Y.; Ho, D.; Howe, A. Y.; Orlowski, M.; Feld, B.; O'Connell, J. Discovery of pyrano[3,4-b]indoles as potent and selective HCV NS5B polymerase inhibitors. J Med Chem 2004, 47, 6603-8. 278. Gopalsamy, A.; Lim, K.; Ellingboe, J. W.; Krishnamurthy, G.; Orlowski, M.; Feld, B.; van Zeijl, M.; Howe, A. Y. Identification of [(naphthalene-1-carbonyl)-amino]-acetic acid 152 BIBLIOGRAPHY derivatives as nonnucleoside inhibitors of HCV NS5B RNA dependent RNA polymerase. Bioorg Med Chem Lett 2004, 14, 4221-4. 279. Gopalsamy, A.; Shi, M.; Ciszewski, G.; Park, K.; Ellingboe, J. W.; Orlowski, M.; Feld, B.; Howe, A. Y. Design and synthesis of 2,3,4,9-tetrahydro-1H-carbazole and 1,2,3,4tetrahydro-cyclopenta[b]indole derivatives as non-nucleoside inhibitors of hepatitis C virus NS5B RNA-dependent RNA polymerase. Bioorg Med Chem Lett 2006, 16, 2532-4. 280. Hecker, S. J.; Reddy, K. R.; van Poelje, P. D.; Sun, Z.; Huang, W.; Varkhedkar, V.; Reddy, M. V.; Fujitaki, J. M.; Olsen, D. B.; Koeplinger, K. A.; Boyer, S. H.; Linemeyer, D. L.; MacCoss, M.; Erion, M. D. Liver-targeted prodrugs of 2'-C-methyladenosine for therapy of hepatitis C virus infection. J Med Chem 2007, 50, 3891-6. 281. Hendricks, R. T.; Fell, J. B.; Blake, J. F.; Fischer, J. P.; Robinson, J. E.; Spencer, S. R.; Stengel, P. J.; Bernacki, A. L.; Leveque, V. J.; Le Pogam, S.; Rajyaguru, S.; Najera, I.; Josey, J. A.; Harris, J. R.; Swallow, S. Non-nucleoside inhibitors of HCV NS5B polymerase. Part 1: Synthetic and computational exploration of the binding modes of benzothiadiazine and 1,4benzothiazine HCV NS5b polymerase inhibitors. Bioorg Med Chem Lett 2009, 19, 3637-41. 282. Hendricks, R. T.; Spencer, S. R.; Blake, J. F.; Fell, J. B.; Fischer, J. P.; Stengel, P. J.; Leveque, V. J.; Lepogam, S.; Rajyaguru, S.; Najera, I.; Josey, J. A.; Swallow, S. 3Hydroxyisoquinolines as inhibitors of HCV NS5b RNA-dependent RNA polymerase. Bioorg Med Chem Lett 2009, 19, 410-4. 283. Howe, A. Y.; Bloom, J.; Baldick, C. J.; Benetatos, C. A.; Cheng, H.; Christensen, J. S.; Chunduru, S. K.; Coburn, G. A.; Feld, B.; Gopalsamy, A.; Gorczyca, W. P.; Herrmann, S.; Johann, S.; Jiang, X.; Kimberland, M. L.; Krisnamurthy, G.; Olson, M.; Orlowski, M.; Swanberg, S.; Thompson, I.; Thorn, M.; Del Vecchio, A.; Young, D. C.; van Zeijl, M.; Ellingboe, J. W.; Upeslacis, J.; Collett, M.; Mansour, T. S.; O'Connell, J. F. Novel nonnucleoside inhibitor of hepatitis C virus RNA-dependent RNA polymerase. Antimicrob Agents Chemother 2004, 48, 4813-21. 284. Howe, A. Y.; Cheng, H.; Thompson, I.; Chunduru, S. K.; Herrmann, S.; O'Connell, J.; Agarwal, A.; Chopra, R.; Del Vecchio, A. M. Molecular mechanism of a thumb domain hepatitis C virus nonnucleoside RNA-dependent RNA polymerase inhibitor. Antimicrob Agents Chemother 2006, 50, 4103-13. 285. Huang, P.; Goff, D. A.; Huang, Q.; Martinez, A.; Xu, X.; Crowder, S.; Issakani, S. D.; Anderson, E.; Sheng, N.; Achacoso, P.; Yen, A.; Kinsella, T.; Darwish, I. S.; Kolluri, R.; Hong, H.; Qu, K.; Stauffer, E.; Goldstein, E.; Singh, R.; Payan, D. G.; Lu, H. H. Discovery and characterization of substituted diphenyl heterocyclic compounds as potent and selective inhibitors of hepatitis C virus replication. Antimicrob Agents Chemother 2008, 52, 1419-29. 286. Ingravallo, P.; Lahser, F.; Xia, E.; Sodowich, B.; Lai, V. C.; Hong, Z.; Zhong, W. Characterization of monoclonal antibodies that specifically recognize the palm subdomain of hepatitis C virus nonstructural protein 5B polymerase. Virus Res 2001, 75, 179-87. 287. Ishida, T.; Suzuki, T.; Hirashima, S.; Mizutani, K.; Yoshida, A.; Ando, I.; Ikeda, S.; Adachi, T.; Hashimoto, H. Benzimidazole inhibitors of hepatitis C virus NS5B polymerase: identification of 2-[(4-diarylmethoxy)phenyl]-benzimidazole. Bioorg Med Chem Lett 2006, 16, 1859-63. 288. Ivanov, A. V.; Smirnova, O. A.; Golubeva, N. A.; Ivanov, M. A.; Tunitskaya, V. L.; Shipitsyn, A. V.; Alexandrova, L. A. Base-modified ribonucleosides as potential antihepatitis C virus agents. Nucleic Acids Symp Ser (Oxf) 2008, 619-20. 289. Ivanov, M. A.; Ivanov, A. V.; Krasnitskaia, I. A.; Smirnova, O. A.; Karpenko, I. L.; Belanov, E. F.; Prasolov, V. S.; Tunitskaia, V. L.; Aleksandrova, L. A. [New furano- and pyrrolo[2,3-d]pyrimidine nucleosides and their 5'-triphosphates: synthesis and biological properties]. Bioorg Khim 2008, 34, 661-70. 153 BIBLIOGRAPHY 290. Kaushik-Basu, N.; Bopda-Waffo, A.; Talele, T. T.; Basu, A.; Chen, Y.; Kucukguzel, S. G. 4-Thiazolidinones: a novel class of hepatitis C virus NS5B polymerase inhibitors. Front Biosci 2008, 13, 3857-68. 291. Kaushik-Basu, N.; Bopda-Waffo, A.; Talele, T. T.; Basu, A.; Costa, P. R.; da Silva, A. J.; Sarafianos, S. G.; Noel, F. Identification and characterization of coumestans as novel HCV NS5B polymerase inhibitors. Nucleic Acids Res 2008, 36, 1482-96. 292. Klumpp, K.; Kalayanov, G.; Ma, H.; Le Pogam, S.; Leveque, V.; Jiang, W. R.; Inocencio, N.; De Witte, A.; Rajyaguru, S.; Tai, E.; Chanda, S.; Irwin, M. R.; Sund, C.; Winqist, A.; Maltseva, T.; Eriksson, S.; Usova, E.; Smith, M.; Alker, A.; Najera, I.; Cammack, N.; Martin, J. A.; Johansson, N. G.; Smith, D. B. 2'-deoxy-4'-azido nucleoside analogs are highly potent inhibitors of hepatitis C virus replication despite the lack of 2'alpha-hydroxyl groups. J Biol Chem 2008, 283, 2167-75. 293. Kneteman, N. M.; Howe, A. Y.; Gao, T.; Lewis, J.; Pevear, D.; Lund, G.; Douglas, D.; Mercer, D. F.; Tyrrell, D. L.; Immermann, F.; Chaudhary, I.; Speth, J.; Villano, S. A.; O'Connell, J.; Collett, M. HCV796: A selective nonstructural protein 5B polymerase inhibitor with potent anti-hepatitis C virus activity in vitro, in mice with chimeric human livers, and in humans infected with hepatitis C virus. Hepatology 2009, 49, 745-52. 294. Koch, U.; Narjes, F. Allosteric inhibition of the hepatitis C virus NS5B RNA dependent RNA polymerase. Infect Disord Drug Targets 2006, 6, 31-41. 295. Kozlov, M. V.; Polyakov, K. M.; Filippova, S. E.; Evstifeev, V. V.; Lyudva, G. S.; Kochetkov, S. N. RNA-dependent RNA polymerase of hepatitis C virus: study on inhibition by alpha,gamma-diketo acid derivatives. Biochemistry (Mosc) 2009, 74, 834-41. 296. Laporte, M. G.; Lessen, T. A.; Leister, L.; Cebzanov, D.; Amparo, E.; Faust, C.; Ortlip, D.; Bailey, T. R.; Nitz, T. J.; Chunduru, S. K.; Young, D. C.; Burns, C. J. Tetrahydrobenzothiophene inhibitors of hepatitis C virus NS5B polymerase. Bioorg Med Chem Lett 2006, 16, 100-3. 297. Lee, G.; Piper, D. E.; Wang, Z.; Anzola, J.; Powers, J.; Walker, N.; Li, Y. Novel inhibitors of hepatitis C virus RNA-dependent RNA polymerases. J Mol Biol 2006, 357, 1051-7. 298. Li, H.; Tatlock, J.; Linton, A.; Gonzalez, J.; Jewell, T.; Patel, L.; Ludlum, S.; Drowns, M.; Rahavendran, S. V.; Skor, H.; Hunter, R.; Shi, S. T.; Herlihy, K. J.; Parge, H.; Hickey, M.; Yu, X.; Chau, F.; Nonomiya, J.; Lewis, C. Discovery of (R)-6-Cyclopentyl-6-(2-(2,6diethylpyridin-4-yl)ethyl)-3-((5,7-dimethyl-[1 ,2,4]triazolo[1,5-a]pyrimidin-2-yl)methyl)-4hydroxy-5,6-dihydropyran-2-on e (PF-00868554) as a Potent and Orally Available Hepatitis C Virus Polymerase Inhibitor. J Med Chem 2009. 299. Liu, L. J.; Hong, J. H. Synthesis and anti-hepatitis C virus activity of 2'(beta)hydroxyethyl and 4'(alpha)-hydroxymethyl carbodine analogues. Nucleosides Nucleotides Nucleic Acids 2009, 28, 1007-15. 300. Louise-May, S.; Yang, W.; Nie, X.; Liu, D.; Deshpande, M. S.; Phadke, A. S.; Huang, M.; Agarwal, A. Discovery of novel dialkyl substituted thiophene inhibitors of HCV by in silico screening of the NS5B RdRp. Bioorg Med Chem Lett 2007, 17, 3905-9. 301. Nyanguile, O.; Devogelaere, B.; Vijgen, L.; Van den Broeck, W.; Pauwels, F.; Cummings, M. D.; De Bondt, H. L.; Vos, A. M.; Berke, J. M.; Lenz, O.; Vandercruyssen, G.; Vermeiren, K.; Mostmans, W.; Dehertogh, P.; Delouvroy, F.; Vendeville, S.; VanDyck, K.; Dockx, K.; Cleiren, E.; Raboisson, P.; Simmen, K. A.; Fanning, G. C. 1a/1b subtype profiling of nonnucleoside polymerase inhibitors of hepatitis C virus. J Virol 2010, 84, 2923-34. 302. Nyanguile, O.; Pauwels, F.; Van den Broeck, W.; Boutton, C. W.; Quirynen, L.; Ivens, T.; van der Helm, L.; Vandercruyssen, G.; Mostmans, W.; Delouvroy, F.; Dehertogh, P.; Cummings, M. D.; Bonfanti, J. F.; Simmen, K. A.; Raboisson, P. 1,5-benzodiazepines, a 154 BIBLIOGRAPHY novel class of hepatitis C virus polymerase nonnucleoside inhibitors. Antimicrob Agents Chemother 2008, 52, 4420-31. 303. Olsen, D. B.; Eldrup, A. B.; Bartholomew, L.; Bhat, B.; Bosserman, M. R.; Ceccacci, A.; Colwell, L. F.; Fay, J. F.; Flores, O. A.; Getty, K. L.; Grobler, J. A.; LaFemina, R. L.; Markel, E. J.; Migliaccio, G.; Prhavc, M.; Stahlhut, M. W.; Tomassini, J. E.; MacCoss, M.; Hazuda, D. J.; Carroll, S. S. A 7-deaza-adenosine analog is a potent and selective inhibitor of hepatitis C virus replication with excellent pharmacokinetic properties. Antimicrob Agents Chemother 2004, 48, 3944-53. 304. Ontoria, J. M.; Martin Hernando, J. I.; Malancona, S.; Attenni, B.; Stansfield, I.; Conte, I.; Ercolani, C.; Habermann, J.; Ponzi, S.; Di Filippo, M.; Koch, U.; Rowley, M.; Narjes, F. Identification of thieno[3,2-b]pyrroles as allosteric inhibitors of hepatitis C virus NS5B polymerase. Bioorg Med Chem Lett 2006, 16, 4026-30. 305. Ontoria, J. M.; Rydberg, E. H.; Di Marco, S.; Tomei, L.; Attenni, B.; Malancona, S.; Martin Hernando, J. I.; Gennari, N.; Koch, U.; Narjes, F.; Rowley, M.; Summa, V.; Carroll, S. S.; Olsen, D. B.; De Francesco, R.; Altamura, S.; Migliaccio, G.; Carfi, A. Identification and Biological Evaluation of a Series of 1H-Benzo[de]isoquinoline-1,3(2H)-diones as Hepatitis C Virus NS5B Polymerase Inhibitors (double dagger). J Med Chem 2009, 52, 5217-27. 306. Pace, P.; Nizi, E.; Pacini, B.; Pesci, S.; Matassa, V.; De Francesco, R.; Altamura, S.; Summa, V. The monoethyl ester of meconic acid is an active site inhibitor of HCV NS5B RNA-dependent RNA polymerase. Bioorg Med Chem Lett 2004, 14, 3257-61. 307. Pacini, B.; Avolio, S.; Ercolani, C.; Koch, U.; Migliaccio, G.; Narjes, F.; Pacini, L.; Tomei, L.; Harper, S. 2-(3-Thienyl)-5,6-dihydroxypyrimidine-4-carboxylic acids as inhibitors of HCV NS5B RdRp. Bioorg Med Chem Lett 2009, 19, 6245-9. 308. Powdrill, M. H.; Deval, J.; Narjes, F.; De Francesco, R.; Gotte, M. Mechanism of hepatitis C virus RNA polymerase inhibition with dihydroxypyrimidines. Antimicrob Agents Chemother 2010, 54, 977-83. 309. Reich, S.; Golbik, R. P.; Geissler, R.; Lilie, H.; Behrens, S. E. Mechanisms of activity and inhibition of the hepatitis C virus RNA-dependent RNA-polymerase. J Biol Chem 2010. 310. Rong, F.; Chow, S.; Yan, S.; Larson, G.; Hong, Z.; Wu, J. Structure-activity relationship (SAR) studies of quinoxalines as novel HCV NS5B RNA-dependent RNA polymerase inhibitors. Bioorg Med Chem Lett 2007, 17, 1663-6. 311. Ryu, K.; Kim, N. D.; Choi, S. I.; Han, C. K.; Yoon, J. H.; No, K. T.; Kim, K. H.; Seong, B. L. Identification of novel inhibitors of HCV RNA-dependent RNA polymerase by pharmacophore-based virtual screening and in vitro evaluation. Bioorg Med Chem 2009, 17, 2975-82. 312. Sarisky, R. T. Non-nucleoside inhibitors of the HCV polymerase. J Antimicrob Chemother 2004, 54, 14-6. 313. Summa, V.; Petrocchi, A.; Matassa, V. G.; Taliani, M.; Laufer, R.; De Francesco, R.; Altamura, S.; Pace, P. HCV NS5b RNA-dependent RNA polymerase inhibitors: from alpha,gamma-diketoacids to 4,5-dihydroxypyrimidine- or 3-methyl-5hydroxypyrimidinonecarboxylic acids. Design and synthesis. J Med Chem 2004, 47, 5336-9. 314. Summa, V.; Petrocchi, A.; Pace, P.; Matassa, V. G.; De Francesco, R.; Altamura, S.; Tomei, L.; Koch, U.; Neuner, P. Discovery of alpha,gamma-diketo acids as potent selective and reversible inhibitors of hepatitis C virus NS5b RNA-dependent RNA polymerase. J Med Chem 2004, 47, 14-7. 315. Tedesco, R.; Shaw, A. N.; Bambal, R.; Chai, D.; Concha, N. O.; Darcy, M. G.; Dhanak, D.; Fitch, D. M.; Gates, A.; Gerhardt, W. G.; Halegoua, D. L.; Han, C.; Hofmann, G. A.; Johnston, V. K.; Kaura, A. C.; Liu, N.; Keenan, R. M.; Lin-Goerke, J.; Sarisky, R. T.; Wiggall, K. J.; Zimmerman, M. N.; Duffy, K. J. 3-(1,1-dioxo-2H-(1,2,4)-benzothiadiazin-3- 155 BIBLIOGRAPHY yl)-4-hydroxy-2(1H)-quinolinones , potent inhibitors of hepatitis C virus RNA-dependent RNA polymerase. J Med Chem 2006, 49, 971-83. 316. Tramontano, E. The exploding field of the HCV polymerase non-nucleoside inhibitors: summary of a first generation compounds. Mini Rev Med Chem 2008, 8, 1298-310. 317. Wang, M.; Ng, K. K.; Cherney, M. M.; Chan, L.; Yannopoulos, C. G.; Bedard, J.; Morin, N.; Nguyen-Ba, N.; Alaoui-Ismaili, M. H.; Bethell, R. C.; James, M. N. Nonnucleoside analogue inhibitors bind to an allosteric site on HCV NS5B polymerase. Crystal structures and mechanism of inhibition. J Biol Chem 2003, 278, 9489-95. 318. Yannopoulos, C. G.; Xu, P.; Ni, F.; Chan, L.; Pereira, O. Z.; Reddy, T. J.; Das, S. K.; Poisson, C.; Nguyen-Ba, N.; Turcotte, N.; Proulx, M.; Halab, L.; Wang, W.; Bedard, J.; Morin, N.; Hamel, M.; Nicolas, O.; Bilimoria, D.; L'Heureux, L.; Bethell, R.; Dionne, G. HCV NS5B polymerase-bound conformation of a soluble sulfonamide inhibitor by 2D transferred NOESY. Bioorg Med Chem Lett 2004, 14, 5333-7. 319. Yap, T. L.; Chen, Y. L.; Xu, T.; Wen, D. Y.; Vasudevan, S. G.; Lescar, J. A multistep strategy to obtain crystals of the dengue virus RNA-dependent RNA polymerase that diffract to high resolution. Acta Crystallogr F 2007, 63, 78-83. 320. Yap, T. L.; Xu, T.; Chen, Y. L.; Malet, H.; Egloff, M. P.; Canard, B.; Vasudevan, S. G.; Lescar, J. Crystal structure of the dengue virus RNA-dependent RNA polymerase catalytic domain at 1.85-Angstrom resolution. Journal of Virology 2007, 81, 4753-4765. 321. Zhong, W.; An, H.; Barawkar, D.; Hong, Z. Dinucleotide analogues as novel inhibitors of RNA-dependent RNA polymerase of hepatitis C Virus. Antimicrob Agents Chemother 2003, 47, 2674-81. 322. Yin, Z.; Chen, Y. L.; Schul, W.; Wang, Q. Y.; Gu, F.; Duraiswamy, J.; Kondreddi, R. R.; Niyomrattanakit, P.; Lakshminarayana, S. B.; Goh, A.; Xu, H. Y.; Liu, W.; Liu, B. P.; Lim, J. Y. H.; Ng, C. Y.; Qing, M.; Lim, C. C.; Yip, A.; Wang, G.; Chan, W. L.; Tan, H. P.; Lin, K.; Zhang, B.; Zou, G.; Bernard, K. A.; Garrett, C.; Beltz, K.; Dong, M.; Weaver, M.; He, H. D.; Pichota, A.; Dartois, V.; Keller, T. H.; Shi, P. Y. An adenosine nucleoside inhibitor of dengue virus. P Natl Acad Sci USA 2009, 106, 20435-20439. 323. Ma, H.; Leveque, V.; De Witte, A.; Li, W.; Hendricks, T.; Clausen, S. M.; Cammack, N.; Klumpp, K. Inhibition of native hepatitis C virus replicase by nucleotide and nonnucleoside inhibitors. Virology 2005, 332, 8-15. 324. Beaulieu, P. L.; Gillard, J.; Bykowski, D.; Brochu, C.; Dansereau, N.; Duceppe, J. S.; Hache, B.; Jakalian, A.; Lagace, L.; LaPlante, S.; McKercher, G.; Moreau, E.; Perreault, S.; Stammers, T.; Thauvette, L.; Warrington, J.; Kukolj, G. Improved replicon cellular activity of non-nucleoside allosteric inhibitors of HCV NS5B polymerase: from benzimidazole to indole scaffolds. Bioorg Med Chem Lett 2006, 16, 4987-93. 325. de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer, J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.; Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Adjabeng, G.; Elworthy, T. R.; Li, J.; Wang, B.; Bamberg, J. T.; Harris, S. F.; Wong, A.; Leveque, V. J.; Najera, I.; Le Pogam, S.; Rajyaguru, S.; Ao-Ieong, G.; Alexandrova, L.; Larrabee, S.; Brandl, M.; Briggs, A.; Sukhtankar, S.; Farrell, R. Non-nucleoside inhibitors of HCV polymerase NS5B. Part 4: structure-based design, synthesis, and biological evaluation of benzo[d]isothiazole-1,1-dioxides. Bioorg Med Chem Lett 2009, 19, 5652-6. 326. de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer, J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.; Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Adjabeng, G.; Elworthy, T. R.; Tracy, J.; Chin, E.; Li, J.; Wang, B.; Bamberg, J. T.; Stephenson, R.; Oshiro, C.; Harris, S. F.; Ghate, M.; Leveque, V.; Najera, I.; Le Pogam, S.; Rajyaguru, S.; Ao-Ieong, G.; Alexandrova, L.; Larrabee, S.; Brandl, M.; Briggs, A.; Sukhtankar, S.; Farrell, R.; Xu, B. Non-nucleoside inhibitors of HCV polymerase NS5B. Part 2: Synthesis and 156 BIBLIOGRAPHY structure-activity relationships of benzothiazine-substituted quinolinediones. Bioorg Med Chem Lett 2009, 19, 3642-6. 327. de Vicente, J.; Hendricks, R. T.; Smith, D. B.; Fell, J. B.; Fischer, J.; Spencer, S. R.; Stengel, P. J.; Mohr, P.; Robinson, J. E.; Blake, J. F.; Hilgenkamp, R. K.; Yee, C.; Zhao, J.; Elworthy, T. R.; Tracy, J.; Chin, E.; Li, J.; Lui, A.; Wang, B.; Oshiro, C.; Harris, S. F.; Ghate, M.; Leveque, V. J.; Najera, I.; Le Pogam, S.; Rajyaguru, S.; Ao-Ieong, G.; Alexandrova, L.; Fitch, B.; Brandl, M.; Masjedizadeh, M.; Wu, S. Y.; de Keczer, S.; Voronin, T. Nonnucleoside inhibitors of HCV polymerase NS5B. Part 3: synthesis and optimization studies of benzothiazine-substituted tetramic acids. Bioorg Med Chem Lett 2009, 19, 5648-51. 328. Kukolj, G.; McGibbon, G. A.; McKercher, G.; Marquis, M.; Lefebvre, S.; Thauvette, L.; Gauthier, J.; Goulet, S.; Poupart, M. A.; Beaulieu, P. L. Binding site characterization and resistance to a class of non-nucleoside inhibitors of the hepatitis C virus NS5B polymerase. J Biol Chem 2005, 280, 39260-7. 329. Leary, J. J.; Wittrock, R.; Sarisky, R. T.; Weinberg, A.; Levin, M. J. Susceptibilities of herpes simplex viruses to penciclovir and acyclovir in eight cell lines. Antimicrob Agents Chemother 2002, 46, 762-8. 330. Kebe, K.; Thiam, M.; Diagne Gueye, N. R.; Diop, H.; Dia, A.; Signate Sy, H.; Charpentier, C.; Belec, L.; Mboup, S.; Toure Kane, C. High rate of antiretroviral drug resistance mutations in HIV type 1-infected Senegalese children in virological failure on firstline treatment according to the World Health Organization guidelines. AIDS research and human retroviruses 2013, 29, 242-9. 331. Aghokeng, A. F.; Mpoudi-Ngole, E.; Chia, J. E.; Edoul, E. M.; Delaporte, E.; Peeters, M. High failure rate of the ViroSeq HIV-1 genotyping system for drug resistance testing in Cameroon, a country with broad HIV-1 genetic diversity. J Clin Microbiol 2011, 49, 1635-41. 332. Adjuvanted hepatitis B vaccine: new drug. Patients with renal failure: similar response rate but fewer boosters needed. Prescrire international 2008, 17, 234-6. 333. Dreyer, G. Acute renal failure: estimated glomerular filtration rate should be entered on drug charts. BMJ 2006, 333, 917. 334. Gallego, O.; Ruiz, L.; Vallejo, A.; Clotet, B.; Leal, M.; Soriano, V. Rate of virological treatment failure and frequencies of drug resistance genotypes among human immunodeficiency virus-positive subjects on antiretroviral therapy in Spain. J Clin Microbiol 2002, 40, 3865-6. 335. Heldal, E.; Arnadottir, T.; Cruz, J. R.; Tardencilla, A.; Chacon, L. Low failure rate in standardised retreatment of tuberculosis in Nicaragua: patient category, drug resistance and survival of 'chronic' patients. The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease 2001, 5, 129-36. 336. Crandall, K. A.; Kelsey, C. R.; Imamichi, H.; Lane, H. C.; Salzman, N. P. Parallel evolution of drug resistance in HIV: failure of nonsynonymous/synonymous substitution rate ratio to detect selection. Mol Biol Evol 1999, 16, 372-82. 337. Yin, Z.; Chen, Y. L.; Kondreddi, R. R.; Chan, W. L.; Wang, G.; Ng, R. H.; Lim, J. Y. H.; Lee, W. Y.; Jeyaraj, D. A.; Niyomrattanakit, P.; Wen, D. Y.; Chao, A.; Glickman, J. F.; Voshol, H.; Mueller, D.; Spanka, C.; Dressler, S.; Nilar, S.; Vasudevan, S. G.; Shi, P. Y.; Keller, T. H. N-Sulfonylanthranilic Acid Derivatives as Allosteric Inhibitors of Dengue Viral RNA-Dependent RNA Polymerase. Journal of Medicinal Chemistry 2009, 52, 7934-7937. 338. Tuininga, Y. S.; van Veldhuisen, D. J.; Brouwer, J.; Haaksma, J.; Crijns, H. J.; Man in't Veld, A. J.; Lie, K. I. Heart rate variability in left ventricular dysfunction and heart failure: effects and implications of drug treatment. British heart journal 1994, 72, 509-13. 157 BIBLIOGRAPHY 339. Korb, O.; Olsson, T. S.; Bowden, S. J.; Hall, R. J.; Verdonk, M. L.; Liebeschuetz, J. W.; Cole, J. C. Potential and limitations of ensemble docking. J Chem Inf Model 2012, 52, 1262-74. 340. Lu, Y.; Wang, R.; Yang, C. Y.; Wang, S. Analysis of ligand-bound water molecules in high-resolution crystal structures of protein-ligand complexes. J Chem Inf Model 2007, 47, 668-75. 341. Bellocchi, D.; Macchiarulo, A.; Costantino, G.; Pellicciari, R. Docking studies on PARP-1 inhibitors: insights into the role of a binding pocket water molecule. Bioorg Med Chem 2005, 13, 1151-7. 342. Fisher, R. A. The design of experiments. Oliver and Boyde: Edinburgh, London,, 1935; p xi, 252 p. 343. Davies, M. C.; Alexander, M. R.; Hook, A. L.; Yang, J.; Mei, Y.; Taylor, M.; Urquhart, A. J.; Langer, R.; Anderson, D. G. High throughput surface characterization: A review of a new tool for screening prospective biomedical material arrays. Journal of drug targeting 2010, 18, 741-51. 344. Harbinson, J.; Prinzenberg, A. E.; Kruijer, W.; Aarts, M. G. High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement. Curr Opin Biotechnol 2012, 23, 221-6. 345. Lessman, C. A. The developing zebrafish (Danio rerio): a vertebrate model for highthroughput screening of chemical libraries. Birth defects research. Part C, Embryo today : reviews 2011, 93, 268-80. 346. Kim, M. K.; Jeong, W.; Kang, J.; Chong, Y. Significant enhancement in radicalscavenging activity of curcuminoids conferred by acetoxy substituent at the central methylene carbon. BIOORGANIC & MEDICINAL CHEMISTRY In Press, Corrected Proof. 347. Shiradkar, M. R.; Akula, K. C.; Dasari, V.; Baru, V.; Chiningiri, B.; Gandhi, S.; Kaur, R. Clubbed thiazoles by MAOS: A novel approach to cyclin-dependent kinase 5/p25 inhibitors as a potential treatment for Alzheimer's disease. BIOORGANIC & MEDICINAL CHEMISTRY 2007, 15, 2601-2610. 348. Nicolaou, K. C.; Pfefferkorn, J. A.; Schuler, F.; Roecker, A. J.; Cao, G. Q.; Casida, J. E. Combinatorial synthesis of novel and potent inhibitors of NADH:ubiquinone oxidoreductase. Chemistry & biology 2000, 7, 979-992. 349. Crane, F. L.; Glenn, J. L.; Green, D. E. Studies on the electron transfer system. IV. The electron transfer particle. Biochim Biophys Acta 1956, 22, 475-87. 350. Wood, E.; Latli, B.; Casida, J. E. Fenazaquin Acaricide Specific Binding Sites in NADH: Ubiquinone Oxidoreductase and Apparently the ATP Synthase Stalk. Pesticide Biochemistry and Physiology 1996, 54, 135-145. 351. Roussel, M. J.; Lanotte, M. Maturation sensitive and resistant t(15;17) NB4 cell lines as tools for APL physiopathology: nomenclature of cells and repertory of their known genetic alterations and phenotypes. Oncogene 2001, 20, 7287-91. 352. Cohen, Y.; Schuldiner, M. Advanced methods for high-throughput microscopy screening of genetically modified yeast libraries. Methods Mol Biol 2011, 781, 127-59. 353. Segall, M. D. Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr Pharm Des 2012, 18, 1292-310. 354. Nicolaou, C. A.; Kannas, C.; Loizidou, E. Multi-objective optimization methods in de novo drug design. Mini Rev Med Chem 2012, 12, 979-87. 355. Sanchez-Faddeev, H.; Emmerich, M. M.; Verbeek, F.; Henry, A.; Grimshaw, S.; Spaink, H.; Vlijmen, H.; Bender, A. Using Multiobjective Optimization and Energy Minimization to Design an Isoform-Selective Ligand of the 14-3-3 Protein. In Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies, Margaria, T.; Steffen, B., Eds. Springer Berlin Heidelberg: 2012; Vol. 7610, pp 12-24. 158 BIBLIOGRAPHY 356. Fung, C.-P. Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis. Wear 2003, 254, 298306. 357. Kim, H. R.; Park, Y. W.; Lee, K. Y. Application of grey relational analysis to determine welding parameters for Nd:YAG laser GMA hybrid welding of aluminium alloy. Sci. Technol. Weld. Joining 2008, 13, 312-317. 358. Singh, P. N.; Raghukandan, K.; Pai, B. C. Optimization by Grey relational analysis of EDM parameters on machining Al-10%SiCP composites. J. Mater. Process. Technol 2004, 155-156, 1658-1661. 359. Aggarwal, A.; Singh, H.; Kumar, P.; Singh, M. Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique-A comparative analysis. J. Mater. Process. Technol 2008, 200, 373-384. 360. Thomas, C. R.; George, S.; Horst, A. M.; Ji, Z.; Miller, R. J.; Peralta-Videa, J. R.; Xia, T.; Pokhrel, S.; Madler, L.; Gardea-Torresdey, J. L.; Holden, P. A.; Keller, A. A.; Lenihan, H. S.; Nel, A. E.; Zink, J. I. Nanomaterials in the environment: from materials to highthroughput screening to organisms. ACS nano 2011, 5, 13-20. 361. Eglen, R.; Reisine, T. Primary cells and stem cells in drug discovery: emerging tools for high-throughput screening. Assay Drug Dev Technol 2011, 9, 108-24. 362. Raucy, J. L.; Lasker, J. M. Current in vitro high throughput screening approaches to assess nuclear receptor activation. Current drug metabolism 2010, 11, 806-14. 363. Evensen, L.; Link, W.; Lorens, J. B. Imaged-based high-throughput screening for anti-angiogenic drug discovery. Curr Pharm Des 2010, 16, 3958-63. 364. Heeres, J. T.; Hergenrother, P. J. High-throughput screening for modulators of protein-protein interactions: use of photonic crystal biosensors and complementary technologies. Chem Soc Rev 2011, 40, 4398-410. 159 APPENDICES APPENDICES APPENDIX 1: ENRICHMENT RESULTS Table S1.1 The top 10 PubChem-MACCS fingerprint keys/substructures selected for the respective datasets. Rank EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Test Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Test Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Test Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Test Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Test Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Test Set 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP438 PubchemFP386 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP385 PubchemFP572 PubchemFP386 PubchemFP491 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP438 PubchemFP491 PubchemFP386 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP445 PubchemFP484 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP530 PubchemFP16 PubchemFP445 PubchemFP443 PubchemFP528 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP530 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP180 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP385 PubchemFP372 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 MACCSFP154 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP491 PubchemFP445 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP491 PubchemFP445 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 PubchemFP431 MACCSFP104 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP431 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 PubchemFP431 MACCSFP104 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP346 None MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 MACCSFP104 PubchemFP145 PubchemFP403 None MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP431 PubchemFP403 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP596 PubchemFP16 PubchemFP674 PubchemFP521 PubchemFP621 PubchemFP379 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP674 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP145 PubchemFP530 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP576 PubchemFP482 MACCSFP106 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP636 MACCSFP107 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 PubchemFP577 MACCSFP62 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP577 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 PubchemFP577 MACCSFP62 PubchemFP636 PubchemFP373 PubchemFP674 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP491 PubchemFP379 MACCSFP62 PubchemFP577 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP577 PubchemFP636 160 APPENDICES Table S1.2 The cumulative percentage of potent compounds picked up (by PubChem-MACCS fingerprint keys) at each decile. Decile 10 EGFR Validation Set 49.7% 75.5% 85.2% 87.7% 89.0% 93.4% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.4% 78.0% 83.7% 87.2% 88.5% 97.8% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.3% 75.9% 85.1% 87.0% 87.6% 89.8% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 51.4% 78.5% 85.2% 86.8% 88.3% 89.6% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.4% 77.8% 85.4% 87.9% 88.6% 89.8% 100.0% 100.0% 100.0% 100.0% EGFR Test Set 52.4% 73.7% 84.3% 87.1% 87.8% 90.0% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 60.8% 81.6% 88.5% 97.7% 99.5% 99.5% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 56.2% 74.2% 84.3% 90.8% 97.2% 98.2% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 65.4% 81.1% 91.7% 92.6% 94.0% 100.0% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 51.4% 75.2% 82.1% 94.0% 95.0% 97.2% 97.7% 99.5% 99.5% 100.0% SRC Validation Set 63.3% 77.1% 89.0% 94.0% 95.9% 96.3% 100.0% 100.0% 100.0% 100.0% SRC Test Set 64.5% 77.4% 86.6% 89.4% 97.2% 97.2% 97.2% 100.0% 100.0% 100.0% AKT1 Validation Set 96.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 79.8% 91.3% 93.3% 93.3% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 80.8% 95.2% 96.2% 96.2% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 89.4% 95.2% 96.2% 97.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 88.6% 95.2% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Test Set 80.6% 94.2% 94.2% 95.1% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% PKCβ Validation Set 56.1% 84.8% 86.4% 93.9% 95.5% 97.0% 97.0% 97.0% 100.0% 100.0% PKCβ Validation Set 59.1% 83.3% 89.4% 93.9% 97.0% 97.0% 98.5% 98.5% 100.0% 100.0% PKCβ Validation Set 54.5% 83.3% 86.4% 95.5% 98.5% 98.5% 98.5% 100.0% 100.0% 100.0% PKCβ Validation Set 84.8% 90.9% 93.9% 97.0% 98.5% 98.5% 100.0% 100.0% 100.0% 100.0% PKCβ Validation Set 63.6% 81.8% 89.4% 90.9% 93.9% 97.0% 97.0% 98.5% 98.5% 100.0% PKCβ Test Set 57.6% 78.8% 86.4% 90.9% 93.9% 95.5% 98.5% 100.0% 100.0% 100.0% CDK2 Validation Set 36.1% 52.6% 70.3% 88.0% 92.5% 93.6% 99.2% 100.0% 100.0% 100.0% CDK2 Validation Set 34.4% 50.9% 71.8% 88.3% 93.8% 95.2% 100.0% 100.0% 100.0% 100.0% CDK2 Validation Set 37.2% 56.9% 75.9% 89.1% 94.9% 97.8% 98.2% 100.0% 100.0% 100.0% CDK2 Validation Set 31.3% 54.1% 69.8% 86.6% 92.9% 96.3% 100.0% 100.0% 100.0% 100.0% CDK2 Validation Set 34.8% 53.8% 73.5% 92.4% 95.1% 96.6% 98.9% 100.0% 100.0% 100.0% CDK2 Test Set 33.2% 52.3% 72.1% 88.2% 93.1% 95.4% 100.0% 100.0% 100.0% 100.0% p38α Validation Set 36.3% 57.7% 70.3% 83.0% 89.9% 93.4% 98.4% 100.0% 100.0% 100.0% p38α Validation Set 28.5% 58.2% 68.7% 80.8% 89.2% 94.1% 98.8% 100.0% 100.0% 100.0% p38α Validation Set 29.5% 57.1% 70.2% 84.5% 90.4% 95.0% 97.8% 100.0% 100.0% 100.0% p38α Validation Set 25.2% 50.9% 66.1% 77.0% 88.2% 92.5% 97.8% 100.0% 100.0% 100.0% p38α Validation Set 30.8% 59.2% 64.7% 81.0% 86.1% 90.9% 97.3% 100.0% 100.0% 100.0% p38α Test Set 24.5% 54.0% 67.5% 77.9% 86.5% 89.6% 96.3% 100.0% 100.0% 100.0% 161 APPENDICES EGFR dataset a) Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 AKT1 dataset c) 10 Fold improvement over random 40 60 Percentage of the dataset (deciles) 80 20 40 60 Percentage of the dataset (deciles) 80 PKC dataset d) 10 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 100 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 40 60 Percentage of the dataset (deciles) CDK2 dataset e) 10 80 100 20 40 60 Percentage of the dataset (deciles) p38a dataset f) 10 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random Fold improvement over random Fold improvement over random Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 100 Fold improvement over random SRC dataset b) 10 Fold improvement over random Fold improvement over random 10 80 100 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 40 60 Percentage of the dataset (deciles) 80 100 20 40 60 Percentage of the dataset (deciles) 80 100 Figure S1.1 Enrichment curve of the five-fold cross validation and external validation. Each plot shows the fold improvement over random selection of active selection of actives for each decile. 162 APPENDICES Table S1.3 The top 10 Klekota-Roth fingerprint keys/substructures selected for the respective datasets. Rank AKT1 Validation Set 1st KR1 2nd KR298 3rd KR296 4th KR1192 5th KR668 6th KR3402 AKT1 Validation Set KR3640 KR3402 KR3882 KR1193 KR3750 None AKT1 Validation Set KR1501 KR2976 KR2975 KR2548 KR3402 KR3025 AKT1 Validation Set KR3402 KR3025 KR1193 KR3926 None None AKT1 Validation Set KR3402 KR3025 KR1193 KR3926 None AKT1 Test Set KR3402 KR3025 KR1193 KR3926 p38α Validation Set KR3956 None None p38α Validation Set KR3956 None p38α Validation Set KR2974 p38α Validation Set p38α Validation Set p38α Test Set 7th KR3025 8th KR3926 9th None 10th None None None None None KR1193 KR3926 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None KR2975 KR4080 KR4330 KR3223 KR4079 KR296 KR3882 KR4331 KR3224 KR3956 None None None None None None None None None KR3956 None None None None None None None None None KR3956 None None None None None None None None None 163 APPENDICES APPENDIX 2: EXPERIMENTAL ACTIVITY DATA Table S2.1. IC50 values of test compounds on APL (NB4) cell line. Mean IC50 (µM)[a] Reference name Linker Substituents APL cell line NB4 Cyclohexanone H 4.50 ± 0.42 Cyclohexanone 3OCH3, 4OH 2.12 ± 0.33 Cyclohexanone 3OH, 4OCH3 1.23 ± 0.33 Cyclohexanone 3OCH3, 4OCH3 4.01 ± 0.24 Cyclohexanone 3OH, 4OH 2.79 ± 0.56 Cyclohexanone 3H, 4OCH3 19.56 ± 1.53 Cyclohexanone 3OCH3, 4H 6.24 ± 0.49 Cyclohexanone 3H, 4OH 2.50 ± 0.11 Cyclohexanone 3OH, 4H 0.98 ± 0.06 10 Thiopyranone 3H, 4OCH3 17.19 ± 4.53 11 Thiopyranone 3OCH3, 4H 2.51 ± 0.28 12 Thiopyranone 3H, 4OH 2.52 ± 0.22 13 Thiopyranone 3OH, 4H 0.51 ± 0.07 14 Thiopyranone H 1.19 ± 0.23 15 Thiopyranone 3OH, 4OH 4.71 ± 0.33 16 Thiopyranone 3OCH3, 4OCH3 0.69 ± 0.02 [a] Mean of three or more independent experiments. 164 [...]... different scoring functions are combined in a variety of ways so as to achieve improvement in the prediction of docked poses and binding affinity 77-87 Despite the availability of all of these different types of scoring functions, the current state of the art of the existing scoring functions is still unable to reliably predict the native binding mode and 5 CHAPTER 1 associate free energy of binding 88 This... specifically intended for organizing, modelling and analysis of chemical entities Such tools are primarily concerned with designing novel compounds, 10 identifying the most probable lead candidates 11-14 and providing a deeper understanding of the protein-ligand interactions that are responsible for their known biological activities 15-17 2 CHAPTER 1 1.2 VIRTUAL SCREENING One of the essential aspects in CADD... virtual screening Virtual screening 18 is the computational technique that deals with the rapid identification of the compounds of interest from a large compound library The goal of virtual screening is to filter, score and rank structures of compounds using in silico methods Virtual screening may be used to select and prioritize compounds for screening in assays, 19 selecting which compounds to acquire... molecular docking, are able to provide crucial insights into the type of interactions between drug targets and the ligands 1.3 MOLECULAR DOCKING & SCORING FUNCTIONS Molecular docking is commonly used to identify potential active compounds by ranking a library of compounds based on the strength of protein-ligand interactions which are evaluated via a scoring function 38, 39 During the docking process,... prioritization of compounds in drug discovery The research work has been allocated into four parts, each catering to a different stage of the drug discovery process In the first part of the thesis, the objective was to formulate a computational workflow that can be used to prioritize compounds of interest from a primary screen hit list for reconfirmation screening, an important step in initiating lead discovery. .. different ligand orientations and conformations (collectively known as docked poses) in the binding pocket of the target macromolecule 40 Molecular docking methods allow different levels of flexibility for the protein and ligands It is commonplace for recent docking algorithms to allow complete flexibility for the ligands To a lesser extent, different levels of flexibility to side chains of the amino acid... xvi LIST OF FIGURES Figure 1.1 Stages of drug discovery and development 1 Figure 2.1 Typical workflow of compound selection and screening in the pharmaceutical industry 17 Figure 2.2 Idealised Gaussian distribution and an indication of the top X% of compounds (area under curve) 20 Figure 2.3 Idealised Gaussian distribution and an indication of n percent inhibition... 1.1 INTRODUCTION TO COMPUTATIONAL METHODS IN DRUG DISCOVERY INTRODUCTION Figure 1.1 Stages of drug discovery and development Before work is started to discover any potential new medicine for a specific disease, scientists need to investigate the underlying cause of the disease as thoroughly as possible In particular, they seek to understand how genes are altered and the related mechanism of action of. .. It will be subjected to extensive in vitro and in vivo testing to determine if it is safe enough for human testing In the next step, the candidate drug enters the development process (clinical trials) in which it will be tested in humans for its efficacy and safety Novel drug discovery and development is known to be lengthy, risky and costly It takes around 14 years 1 and up to US$1.3 billion 2 from... since it is critical for the replication of the dengue virus’ RNA In this work, a virtual screening workflow was formulated A virtual screening protocol was formulated that included docking, pharmacophoric and shape based matching techniques for the analysis of the interactions of a corporate database against the enzymatic target In the final part of the thesis, a novel application of the Taguchi Method . IN SILICO METHODOLOGIES FOR SELECTION AND PRIORITIZATION OF COMPOUNDS IN DRUG DISCOVERY YEO WEE KIANG (M.Sc. (Bioinformatics), NTU) A THESIS SUBMITTED FOR THE. thesis was to investigate the various methodologies that can be applied for the selection and prioritization of compounds in drug discovery. The research work has been allocated into four parts,. States of America). Poster title: “A Random Forest Clustering Approach to Compound Selection and Prioritization for High-Throughput Screening Campaigns”. 4. The 7th International Symposium for