The past approach of high-throughput screening of everything in the corporate collection has been shown to be very expensive in terms of reagents cost, disposal cost, and compound collection depletion. It is well known that screening campaigns produce several hits, ofwhich only 50% confirmon average. More efficientways of screening can provide an informative structure-activity relationship (SAR), which in turn can be used to buildmathematical models for further probing the activity space and directing chemical synthesis. The authors report new methods and insights to extract themaximum possible information from a screening experiment and findmost of the possible hits in the corporate collection while screening as few compounds as possible.
Young SS, Sacks J: Analysis of a large, high-throughput screening data using recursive partitioning. Paper presented at the Proceedings of the 12th European Symposium on Quantitative Structure-Activity Relationships: Molecu-lar Modeling and Prediction of Bioactivity, Copenhagen, Denmark, August 1998.
2.
Valler MJ, Green D: Diversity screening versus focused screening in drugdiscovery. J Med Chem2000;5:286-293.
3.
Hodgkin E, Andrews-Cramer K: Compound collections get focused. Modern Drug Discov2001;3:55-59.
4.
Fay N, Ullman D: Leveraging process integration in early drug discovery. Drug Discov Today2002;2:181-186.
5.
Engels MFM, Venkatarangan P: Smart screening: approaches to efficient HTS. Curr Opin Drug Discov Devel2001;4:275-283.
6.
Engels MFM, Thielemans T, Verbinnen D, Tollenaere JP, Verbeeck R: Cer-Beru S: a system supporting the sequential screening process. J Chem Inf Comput Sci2000;40:241-245.
7.
Dixon SL, Villar HO: Bioactive diversity and screening library selection via affinity fingerprinting. J Chem Inf Comput Sci1998;38:1192-1203.
8.
Bajorath J: Integration of virtual and high-throughput screening. Nat Rev Drug Discov2002;1:882-894.
9.
Nicolaou CA, Tamura SY, Kelley BP, Bassett SI, Nutt RF: Analysis of large screening data sets via adaptively grown phylogenetic-like trees. J Chem Inf Comput Sci2002;42:1069-1079.
10.
Abt M, Lim Y, Sacks J, Xie M, Young S: A sequential approach for identifying lead compounds in large chemical databases. National Institute of Statistical Science Technical Report No. 105. Accessed from www.niss.org/technicalreports.html
11.
Rusinko A, Farmen MW, Lambert CG, Brown PL, Young SS: Analysis of a large structure/biological activity data set using recursive partitioning. JChem Inf Comput Sci1999;39:1017-1026.
12.
Jones-Hertzog DK, Mukhopadhyay P, Keefer CE, Young SS: Use of recursive partitioning in the sequential screening of G-protein coupled receptors. J Pharmacol Toxicol Methods1999;42:207-215.
13.
Polanski J, Jarzembek K, Gasteiger J: Self-organizing neutral networks for screening and development of novel artificial sweetener candidates. Comb Chem High Throughput Screen2000;3:481-495.
14.
Wild DJ, Blankley CJ: Visuali SAR: aWeb based application for clustering, structure browsing and structure-activity relationship. J Mol Graph Model1999;17:85-89.
15.
Maggiora GM, Johnson MA: Concepts and Applications ofMolecular Similarity. New York: John Wiley, 1990.
16.
Hodes L: Clustering a large number of compounds: 1. Establishing the method on an initial sample. J Chem Inf Comput Sci1989;29:66-71.
17.
Dunbar JB: Cluster-based selection. Perspect Drug Discov Des1997;7/8:51-63.
18.
Ward J: Hierarchical grouping to optimize an objective function. J Am Stat Assoc1963;58:326-344.
19.
Brown RD, Martin YC: Use of structure activity data to compare structurebased clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci1996;36:572-584.
20.
Bayada DM, Hamersma H, van Geerestein VJ: Molecular diversity and representativity in chemical databases. J Chem Inf Comput Sci1999;39:1-10.
21.
BCIC lustering Package, v. 2.5& 3.0. Sheffield, UK: Barnard Chemical Information Ltd., 1998.
22.
Young SS, Lam LLH, Welsh WJ: Initial compound selection for sequential screening. Curr Opin Drug Discov Devel2002;5:422-427.
23.
Kelley LA, Gardner SP, Sutcliffe MJ: An automated approach for clustering an ensemble ofNMR-derived protein structures into conformationally related subfamilies. Protein Eng1996;9:1063-1065.
24.
Daylight Fingerprint Manual, v. 4.51. Irvine, CA: Daylight Chemical Information Systems, 1997.
25.
MDL Information Systems. San Leandro, CA.
26.
Hall LH, Kier LB: Electrotopological state indices for atom types: a novel combination of electronic, topological and valence state information. JChem Inf Comput Sci1995;35:1039-1045.
27.
Cerius2, v. 4.8.San Diego: Accelrys, 2002.
28.
Labute P: Binary QSAR: a new method for the determination of quantitative structure-activity relationship. Paper presented at the 4th Pacific Symposium on Biocomputing, Hawaii, January 1999.
29.
Molecular Operating Environment, v. 2002.01. Montreal, Quebec: Chemical Computing Group, 2002.
30.
van Rhee AM, Stocker J, Printzenhoff D, Creech C, Wagoner PK, Spear KL: Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning. J Comb Chem2001;3:267-277.
31.
Cramer RD, Bunce JD, Patterson DE, Frank IE: Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant Struct Act Relat1988;7:18-25.
32.
Engels MFM: Sequential screening of large compound libraries. Paper presented at the 14th Molecular Modeling Workshop, Darmstadt, Germany, May 2000.
