Selecting, Acquiring, and Using Small Molecule Libraries for High‐Throughput Screening

Sivaraman Dandapani1, Gerard Rosse2, Noel Southall3, Joseph M. Salvino4, Craig J. Thomas3

1 Chemical Biology Platform, The Broad Institute of MIT and Harvard University, Cambridge Massachusetts, 2 Dart NeuroScience, San Diego, California, 3 NIH Chemical Genomics Center, National Human Genome Research Institute, Bethesda, Maryland, 4 Drexel University College of Medicine, Philadelphia, Pennsylvania
Publication Name:  Current Protocols in Chemical Biology
Unit Number:   
DOI:  10.1002/9780470559277.ch110252
Online Posting Date:  September, 2012
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library

Abstract

The selection, acquisition, and use of high‐quality small molecule libraries for screening is an essential aspect of drug discovery and chemical biology programs. Screening libraries continue to evolve as researchers gain a greater appreciation of the suitability of small molecules for specific biological targets, processes, and environments. The decision surrounding the makeup of any given small molecule library is informed by a multitude of variables, and opinions vary on best practices. The fitness of any collection relies upon upfront filtering to avoid problematic compounds, assess appropriate physicochemical properties, install the ideal level of structural uniqueness, and determine the desired extent of molecular complexity. These criteria are under constant evaluation and revision as academic and industrial organizations seek out collections that yield ever‐improving results from their screening portfolios. Practical questions including cost, compound management, screening sophistication, and assay objective also play a significant role in the choice of library composition. This overview attempts to offer advice to all organizations engaged in small molecule screening based upon current best practices and theoretical considerations in library selection and acquisition. Curr. Protoc. Chem. Biol. 4:177‐191 © 2012 by John Wiley & Sons, Inc.

Keywords: small molecule library; high‐throughput screening; chemical biology; drug discovery

     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Table of Contents

  • High‐Throughput Screening as a Means to Discover Bioactive Agents
  • Historical Context of Screening Libraries
  • The Need for High‐Quality Screening Libraries
  • Using Cheminformatics Tools/Filters to Craft a Library
  • Specialty Libraries/Methodologies
  • Compound Sourcing
  • Specialty Libraries
  • Compound Management
  • Library QC and Profiling
  • Conclusion
  • Acknowledgements
  • Literature Cited
  • Figures
  • Tables
     
 
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Materials

GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library

Figures

Videos

Literature Cited

Literature Cited
   Ambesi‐Impiombato, A. and di Bernardo, D. 2006. Computational biology and drug discovery: From single‐target to network drugs. Curr. Bioinform. 1:3‐13.
   Archer, J.R. 2004. History, evolution, and trends in compound management for high throughput screening. Assay Drug Dev. Tech. 2:675‐681.
   Aronov, A.M. 2005. Predictive in silico modeling for hERG channel blockers. Drug Discov. Today 10:149‐155.
   Auld, D.S., Thorne, N., Nguyen, D.T., and Inglese, J. 2008a. A specific mechanism for nonspecific activation in reporter‐gene assays. ACS Chem. Biol. 3:463‐470.
   Auld, D.S., Zhang, Y.‐Q., Southall, N.T., Rai, G., Landsman, M., MacLure, J., Langevin, D., Thomas, C.J., Austin, C.P., and Inglese, J. 2008b. Characterization of chemical libraries for luciferase inhibitory activity. J. Med. Chem. 51:2372‐2386.
   Austin, C.P., Brady, L.S., Insel, T.R., and Collins, F.S. 2004. NIH molecular libraries initiative. Science 306:1138‐1139.
   Babaoglu, K., Simeonov, A., Irwin, J.J., Nelson, M.E., Feng, B., Thomas, C.J., Canian, L., Costi, P., Maltby, D.A., Jadhav, A., Inglese, J., Austin, C.P., and Shoichet, B.K. 2008. Comprehensive mechanistic analysis of hits from high‐throughput and docking screens against β‐lactamase. J. Med. Chem. 51:2502‐2511.
   Baell, J.B. and Holloway, G.A. 2010. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries for their exclusion in bioassays. J. Med. Chem. 53:2719‐2740.
   Bajorath, J. 2002. Integration of virtual and high‐throughput screening. Nat. Rev. Drug Disc. 1:882‐892.
   Balakin, K.V., Savchuk, N.P., and Tetko, I.V. 2006. In silico approaches to prediction of aqueous and DMSO solubility of drug‐like compounds: Trends, problems and solutions. Curr. Med. Chem. 13:223‐241.
   Bender, A., Jenkins, J.L., Scheiber, J., Sukuru, S.C.K., Glick, M., and Davies, J.W. 2009. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J. Chem. Inf. Model. 49:108‐119.
   Bleicher, K.H., Böhm, H.‐J., Müller, K., and Alanine, A.I. 2003. Hit and lead generation: Beyond high‐throughput screening. Nat. Rev. Drug Disc. 2:369‐378.
   Bova, M.P., Mattson, M.N., Vasile, S., Tam, D., Holsinger, L., Bremer, M., Hui, T., McMahon, G., Rice, A., and Fukuto, J.M. 2004. The oxidative mechanism of action of ortho‐quinone inhibitors of protein‐tyrosine phosphatase (alpha) is mediated by hydrogen peroxide. Arch. Biochem. Biophys. 429:30‐41.
   Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I.H., Frearson, J., and Wyatt, P.G. 2008. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3:435‐444.
   Bunin, B.A., Plunkett, M.J., and Ellman, J.A. 1994. The combinatorial synthesis and chemical and biological evaluation of a 1,4‐benzodiazepine library. Proc. Natl. Acad. Sci. U.S.A. 91:4708‐4712.
   Burke, M.D. and Schreiber, S.L. 2004. A planning strategy for diversity‐oriented synthesis. Angew. Chem. Int. Ed. 43:46‐58.
   Butler, M.S. 2004. The role of natural product chemistry in drug discovery. J. Nat. Prod. 67:2141‐2153.
   Clark, D.E. 2003. In silico prediction of blood‐brain barrier permeation. Drug Discov. Today 8:927‐933.
   Clemons, P.A., Wilson, J.A., Dancik, V., Muller, S., Carrinski, H.A., Wagner, B.K., Koehler, A.N., and Schreiber, S.L. 2011. Quantifying structure and performance diversity for sets of small molecules comprising small‐molecule screening collections. Proc. Natl. Acad. Sci. U.S.A. 108:6817‐6822.
   Congreve, M., Chessari, G., Tisi, D., and Woodhead, A.J. 2008. Recent developments in fragment‐based drug discovery. J. Med. Chem. 51:3661‐3680.
   Cox, J. and Mann, M. 2007. Is proteomics the new genomics? Cell 130:395‐398.
   Delaney, J.S. 2004. ESOL: Estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comp. Sci. 44:1000‐1005.
   Demel, M.A., Schwaha, R., Krämer, O., Ettmayer, P., Haaksma, E.E.J., and Ecker, G.F. 2008. In silico prediction of substrate properties for ABC‐multidrug transporters. Expert Opin. Drug Metab. Toxicol. 4:1167‐1180.
   Di, L. and Kerns, E.H. 2006. Biological assay challenges from compound solubility: Strategies for bioassay optimization. Drug Discov. Today 11:446‐451.
   Djuric, S.W., Akritopoulou‐Zanze, I., Cox, P.B., and Galasinski, G. 2010. Compound collection enhancement and paradigms for high‐throughput screening an update. Ann. Rep. Med. Chem. 45:409‐428.
   Drewry, D.H. and Macarron, R. 2010. Enhancements of screening collections to address areas of unmet medical need: An industry perspective. Curr. Opin. Chem. Biol. 14:289‐298.
   Duan, J.X., Dixon, S.L., Lowrie, J.F., and Sherman, W. 2010. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J. Mol. Graphics Model. 29:157‐170.
   Egan, W.J., Merz, K.M., and Baldwin, J.J. 2000. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43:3867‐3877.
   Erlanson, D.A., McDowell, R.S., and O'Brian, T. 2004. Fragment‐based drug discovery. J. Med. Chem. 47:3463‐3482.
   Feng, B.Y., Simeonov, A., Jadhav, A., Babaoglu, K., Inglese, J., Shoichet, B.K., and Austin, C.P. 2007. A high‐throughput screen for aggregation‐based inhibition in a large compound library. J. Med. Chem. 50:2385‐2390.
   Forman, H., Maiorino, M., and Ursini, F. 2010. Signaling functions of reactive oxygen species. Biochemistry 49:835‐842.
   Ghose, A.K., Viswanadhan, V.N., and Wendoloski, J.J. 1999. A knowledge‐based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem. 1:55‐68.
   Guha, R., Dexheimer, T., Kestranek, A.N., Jadhav, A., Chervenak, A.M., Ford, M.G., Simeonov, A., Roth, G.P., and Thomas, C.J. 2011. Exploratory analysis of kinetic solubility measurements of a small molecule library. Bioorg. Med. Chem. 19:4127‐4134.
   Hajduk, P.J. and Greer, J. 2007. A decade of fragment‐based drug design: Strategic advances and lessons learned. Nat. Rev. Drug Discov. 6:211‐219.
   Harris, C.J., Hill, R.D., Sheppard, D.W., Slater, M.J., and Stouten, P.F.W. 2011. The design and application of target‐focused compound libraries. Comb. Chem. High T. Scr. 14:521‐531.
   Hergenrother, P.J. 2006. Obtaining and screening compound collections: a user's guide and a call to chemists. Curr. Opin. Chem. Biol. 10:213‐218.
   Hert, J., Willett, P., and Wilton, D.J. 2004. Comparison of fingerprint‐based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comput. Sci. 44:1177‐1185.
   Hewitt, M., Cronin, M.T.D., Enoch, S.J., Madden, J.C., Roberts, D.W., and Dearden, J.C. 2009. In silico prediction of aqueous solubility: The solubility challenge. J. Chem. Inf. Comp. Sci. 49:2572‐2587.
   Hill, A.P. and Young, R.J. 2010. Getting physical in drug discovery: A contemporary perspective on solubility and hydrophobicity. Drug Dis. Today 15:648‐655.
   Holden, K. 2003. The significance of effective compound management. Curr. Drug. Discov. 9:9‐10.
   Holland‐Crimmin, S., Gosnell, P., and Quinn, C. 2011. Compound management: Guidelines for compound storage, provision, and quality control. Curr. Protoc. Chem. Biol. 3:141‐152.
   Hou, T., Li, Y., Zhang, W., and Wang, J. 2009. Recent developments of in silico predictions of intestinal absorption and oral bioavailability. Comb. Chem. High. T. Scr. 12:497‐506.
   Houghten, R.A., Pinilla, C., Blondelle, S.E., Appel, J.R., Dooley, C.T., and Cuervo, J.H. 1991. Generation and use of synthetic peptide combinatorial libraries for basic research and drug discovery. Nature 354:84‐86.
   Huang, R., Southall, N., Wang, Y., Yasgar, A., Shinn, P., Jadhav, A., Nguyen, D.T., and Austin, C.P. 2011. The NCGC pharmaceutical collection: A comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci. Transl. Med. 3:16.
   Huggins, D.J., Venkitaraman, A.R., and Spring, D.R. 2011. Rational methods for the selection of diverse screening compounds. ACS Chem. Biol. 6:208‐217.
   Inglese, J., Auld, D.S., Jadhav, A., Johnson, R.L., Simeonov, A., Yasgar, A., Zheng, W., and Austin, C.P. 2006. Quantitative high‐throughput screening: A titration‐based approach that efficiently identifies biological activities in large chemical libraries. Proc. Natl. Acad. Sci. U.S.A. 103:11473‐11478.
   Inglese, J., Johnson, R.L., Simeonov, A., Xia, M., Austin, C.P., and Auld, D.S. 2007. High‐throughput screening assays for the identification of chemical probes. Nat. Chem. Biol. 3:466‐479.
   Jadhav, A., Ferreira, R. S., Klumpp, C., Mott, B.T., Austin, C.P., Inglese, J., Thomas, C.J., Maloney, D.J., Shoichet, B.K., and Simeonov, A. 2010. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. J. Med. Chem. 53:37‐51.
   Janzen, W.P. and Popa‐Burke, I.G. 2009. Advances in improving the quality and flexibility of compound management. J. Biomol. Screen. 14:444‐451.
   Johnson, S.R., Chen, X.‐Q., Murphey, D., and Gudmundsson, O. 2007. A computational model for the prediction of aqueous solubility that includes crystal packing, intrinsic solubility, and ionization effects. Mol. Pharm. 4:513‐523.
   Johnston, P.A. 2011. Redox cycling compounds generate H2O2 in HTS buffers containing strong reducing reagents—real hits or promiscuous artifacts. Curr. Opin. Chem. Biol. 15:174‐182.
   Kalyaanamoorthy, S. and Chen, Y.‐P.P. 2011. Structure‐based drug design to augment hit discovery. Drug Discov. Today 16:831‐839.
   Keighley, W.W. and Wood, T.P. 2002. Compound library management: An overview of an automated system. Methods Mol. Biol. 190:129‐152.
   Kennedy, J.P., Williams, L., Bridges, T.M., Daniels, R. N., Weaver, D., and Lindsley, C.W. 2008. Application of combinatorial chemistry science on modern drug discovery. J. Comb. Chem. 10:345‐354.
   Kim, J., Tang, J.Y., Gong, R., Kim, J., Lee, J.J., Clemons, K.V., Chong, C.R., Chang, K.S., Fereshteh, M., Gardner, D., Reya, T., Liu, J.O., Epstein, E.H., Stevens, D.A., and Beachy, P.A. 2010. Itraconazole, a commonly used antifungal that inhibits hedgehog pathway activity and cancer growth. Cancer Cell 17:388‐399.
   Kitchen, D.B., Decornez, H., Furr, J.R., and Bajorath, J. 2004. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Disc. 3:935‐949.
   Koehn, F.E. and Carter, G.T. 2005. The evolving role of natural products in drug discovery. Nat. Rev. Drug Discov. 4:206‐220.
   Kozikowski, B.A, Burt, T.M., Tirey, D.A., Williams, L.E., Kuzmak, B.R., Stanton, D.T., Morand, K.L., and Nelson, S.L. 2003. The effect of freeze/thaw cycles on the stability of compounds in DMSO. J. Biomol. Screen. 8:210‐215.
   Kramer, C., Heinisch, T., Fligge, T., Beck, B., and Clark, T. 2009. A consistent dataset of kinetic solubilities for early‐phase drug discovery. ChemMedChem 4:1529‐1536.
   Kramer, R. and Cohen, D. 2004. Functional genomics to new drug targets. Nat. Rev. Drug Disc. 3:965‐972.
   Kruglov, A.G., Subbotina, K.B., and Saris, N.‐E.L. 2008. Redox‐cycling compounds can cause the permeabilization of mitochondrial membranes by mechanisms other than ROS production. Free Radical Biol. Med. 44:646‐656.
   Kumar, K. and Waldmann, H. 2009. Synthesis of natural product inspired compound collections. Angew. Chem. Int. Ed. 48:3224‐3242.
   Lal, M., Rao, R., Fang, X., Schuchmann, H.P., and Sonntag, C.V. 1997. Radical‐induced oxidation of dithiothreitol in acidic oxygenated solution: a chain reaction. J. Am. Chem. Soc. 119:5735‐5739.
   Leyva, M.J., DeGiacomo, F., Kaltenbach, L.S., Holcomb, J., Zhang, N., Gafni, J., Park, H., Lo, D.C., Salvesen, G.S., Ellerby, L.M., and Ellman, J.A. 2010. Identification and evaluation of small molecule pan‐caspase inhibitors in Huntington's disease models. Chem. Biol. 17:1189‐1200.
   Lipinski, C.A., Lombardo, F., Dominy, B.W., and Feeney, P.J. 2001. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Del. Rev. 46:3‐26.
   Lovering, F., Bikker, J., and Humblet, C. 2009. Escape from flatland: Increasing saturation as an approach to improving clinical success. J. Med. Chem. 52:6752‐6756.
   Lüder, K., Lindfors, L., Westergren, J., Nordholm, S., Persson, R., and Pedersen, M. 2009. In silico prediction of drug solubility: 4. Will simple potentials suffice? J. Comp. Chem. 30:1859‐1871.
   Macarron, R., Banks, M.N., Bojanic, D., Burns, D.J., Cirovic, D.A., Garyantes, T., Green, D.V.S., Hertzberg, R.P., Janzen, W.P., Paslay, J.W., Schopfer, U., and Sittampalam, G.S. 2011. Impact of high‐throughput screening in biomedical research. Nat. Rev. Drug Disc. 10:188‐195.
   MacArthur, R., Leister, W., Veith, H., Shinn, P., Southall, N., Austin, C.P., Inglese, J., and Auld, D.S. 2009. Monitoring compound integrity with cytochrome P450 assays and qHTS. J. Biomol. Screen. 14:538‐546.
   Mahasenan, K.V., Pavlovicz, R.E., Henderson, B.J., González‐Cestari, T.F., Yi, B., McKay, D.B., and Li, C. 2011. Discovery of Novel α4β2 Neuronal nicotinic receptor modulators through structure‐based virtual screening. ACS Med. Chem. Lett. 2:855‐860.
   Mayr, L.M. and Bojanic, D. 2009. Novel trends in high‐throughput screening. Curr. Opin. Pharm. 9:580‐588.
   Mayr, L.M. and Fuerst, P. 2008. The future of high‐throughput screening. J. Biomol. Screen. 13:443‐448.
   McGovern, S.L., Caselli, E., Grigorieff, N., and Shoichet, B.K. 2002. A common mechanism underlying promiscuous inhibitors from virtual and high‐throughput screening. J. Med. Chem. 45:1712‐1722.
   Merrifield, R.B. 1963. Solid phase peptide synthesis. I. The synthesis of a tetrapeptide. J. Am. Chem. Soc. 85:2149‐2154.
   Myers, P.L. 1997. Will combinatorial chemistry deliver real medicines? Curr. Opin. Biotech. 8:701‐707.
   Newman, D.J. and Cragg, G.M. 2007. Natural products as sources of new drugs over the last 25 years. J. Nat. Prod. 70:461‐477.
   Nielsen, T.E. and Schreiber, S.L. 2008. Towards the optimal screening collection: a synthesis strategy. Agnew. Chem. Int. Ed. 47:48‐56.
   Oprea, T.I. 2000. Property distribution of drug‐related chemical databases. J. Comput.‐Aided Mol. Des. 14:251‐264.
   Overington, J.P., Al‐Lazikani, B., and Hopkins, A.L. 2006. How many drug targets are there? Nat. Rev. Drug Discov. 5:993‐996.
   Patterson, A.W., Wood, W.J.L., and Ellman, J.A. 2007. Substrate activity screening (SAS): A general procedure for the preparation and screening of a fragment‐based non‐peptidic protease substrate library for inhibitor discovery. Nat. Protocols 2:424‐433.
   Ray, B. 2001. Value your compound management team! Drug Discov. Today 6:563.
   Pollastri, M.P. 2010. Overview on the Rule of Five. Curr. Protoc. Pharmacol. 49:9.12.1‐9.12.8.
   Reddy, M.M., Wilson, R., Wilson, J., Connell, S., Gocke, A., Hynan, L., German, D., and Kodadek, T. 2011. Identification of candidate IgG biomarkers for Alzheimer's disease via combinatorial library screening. Cell 144:132‐142.
   Refsgaard, H.H.F., Jensen, B.F., Christensen, I.T., Hagen, N., and Brockhoff, P.B. 2006. In silico prediction of cytochrome 450 inhibitors. Drug. Dev. Res. 67:417‐429.
   Rishton, G.M. 2003. Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 8:86‐96.
   Sauer, W.H.B. and Schwarz, M.K. 2003. Molecular shape diversity of combinatorial libraries: A prerequisite for broad bioactivity? J. Chem. Inf. Comput. Sci. 43:987‐1003.
   Schneider, G. 2010. Virtual screening: An endless staircase? Nat. Rev. Drug Disc. 9:273‐276.
   Schreiber, S.L. 2000. Target‐oriented and diversity oriented organic synthesis in drug discovery. Science 287:1964‐1969.
   Schreiber, S.L. 2011. Organic synthesis toward small‐molecule probes and drugs. Proc. Natl. Acad. Sci. U.S.A. 108:6699‐6702.
   Schuffenhauer, A., Ruedisser, S., Marzinzik, A.L., Jahnke, W., Blommers, M., Selzer, P., and Jacoby, E. 2005. Library design for fragment based screening. Curr. Top. Med. Chem. 5:751‐762.
   Shakeel, S., Karim, S., and Ali, A. 2006. Peptide nucleic acid (PNA) a review. J. Chem. Technol. Biotechnol. 81:892‐899.
   Simeonov, A., Jadhav, A., Thomas, C.J., Wang, Y., Huang, R., Southall, N.T., Shinn, P., Smith, J., Austin, C.P., Auld, D.S., and Inglese, J. 2008. Fluorescence spectroscopic profiling of compound libraries. J. Med. Chem. 51:2362‐2371.
   Singh, J., Petter, R.C., and Kluge, A. 2010. Targeted covalent drugs of the kinase family. Curr. Opin. Chem. Biol. 14:475‐480.
   Soares, K., Blackmon, N., Shun, T.Y., Shinde, S.N., Takyi, H.K., Wipf, P., Lazo, J.S., and Johnston, P.A. 2010. Profiling the NIH small molecule repository for compounds that generate H2O2 by redox cycling in reducing environments. Assay Drug Dev. Technol. 8:152‐174.
   Stahura, F.L. and Bajorath, J. 2004. Virtual Screening methods that complement HTS. Comb. Chem. High. T. Scr. 7:259‐269.
   Steffen, A., Kogej, T., Tyrchan, C., and Engkvist, O. 2009. Comparison of molecular fingerprint methods on the basis of biological profile data. J. Chem. Inf. Model. 49:338‐347.
   Stringer, J.R., Bowman, M.D., Weisblum, B., and Blackwell, H.E. 2011. Improved small‐molecule macroarray platform for the rapid synthesis and discovery of antibacterial chalcones. ACS Comb. Sci. 13:175‐180.
   Tan, D.S. 2005. Diversity‐oriented synthesis: Exploring the intersections between chemistry and biology. Nat. Chem. Biol. 1:74‐84.
   Thorne, N., Auld, D.S., and Inglese, J. 2010. Curr. Opin. Chem. Biol. 14:315‐324.
   van de Waterbeemd, H. and Gifford, E. 2003. ADMET in silico modeling: Towards prediction paradise? Nat. Rev. Drug Discov. 2:192‐204.
   Vester, B. and Wengel, J. 2004. LNA (locked nucleic acid): High‐affinity targeting of complementary RNA and DNA. Biochemistry 43:13233‐13241.
   Walters, W.P., Ajay, and Murcko, M.A. 1999. Recognizing molecules with drug‐like properties. Curr. Opin. Chem. Biol. 3:384‐387.
   Walters, W.P. and Murcko, M.A. 2008. Library filtering systems and prediction of drug‐like properties. In Virtual Screening for Bioactive Molecules. Methods and Principles in Medicinal Chemistry pp. 15‐30. Wiley‐VCH, Weinheim, Germany.
   Walters, W.P. and Namchuck, M. 2003. Designing screens: How to make your hits and hit. Nat. Rev. Drug Discov. 2:259‐266.
   Willett, P. 2006. Similarity‐based virtual screening using 2D fingerprints. Drug Discov. Today 11:1046‐1053.
   Xu, J. 2002. A new approach to finding natural chemical structure classes. J. Med. Chem. 45:5311‐5320.
   Yasgar, A., Shinn, P., Jadhav, A., Auld, D., Michael, S., Zheng, W., Austin, C.P., Inglese, J., and Simeonov, A. 2008. Compound management for quantitative high‐throughput screening. J. Assoc. Lab. Auto. 13:79‐89.
   Yin, H., Lee, G.‐I., and Hamilton, A.D. 2007. Alpha‐helix mimetics in drug discovery. In Drug Discovery Research in the Post Genomics Era (Z. Huang, ed.) pp. 280‐298. John Wiley and Sons, Hoboken, N.J.
   Yoo, B. and Kirshenbaum, K. 2008. Peptoid architectures: Elaboration, actuation, and application. Curr. Opin. Chem. Biol. 12:714‐721.
   Zhou, W., Ercan, D., Chen, L., Yun, C.‐H., Li, D., Capelletti, M., Cortot, A.B., Chirieac, L., Iacob, R.E., Padera, R., Engen, J.R., Wong, K.‐K., Eck, M.J., Gray, N.S., and Jänne, P.A. 2009. Novel mutant‐selective EGFR kinase inhibitors against EFGR T790M. Nature 462:1070‐1074.
GO TO THE FULL PROTOCOL:
PDF or HTML at Wiley Online Library