Fox S, Farr-Jones S, Sopchak L, Boggs A, Comley J (2004) High-throughput screening: searching for higher productivity. J Biomol Screen 9:354–358. doi:10.1177/1087057104265290
Goode DR, Totten RK, Heeres JT, Hergenrothert PJ (2008) Identification of promiscuous small molecule activators in high-throughput enzyme activation screens. J Med Chem 51:2346–2349. doi:10.1021/jm701583b
Hertzberg RP, Pope AJ (2000) High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol 4:445–451
Dolle RE (2001) Comprehensive survey of combinatorial library synthesis: 2000. J Comb Chem 3:477–517. doi:10.1021/cc010049g
Houghten RA, Pinilla C, Appel JR, Blondelle SE, Dooley CT, Eichler J, Nefzi A, Ostresh JM (1999) Mixture-based synthetic combinatorial libraries. J Med Chem 42:3743–3778. doi:10.1021/jm990174v
Houghten RA, Pinilla C, Giulianotti MA, Appel JR, Dooley CT, Nefzi A, Ostresh JM, Yu YP, Maggiora GM, Medina-Franco JL, Brunner D, Schneider J (2008) Strategies for the use of mixture-based synthetic combinatorial libraries: scaffold ranking, direct testing, in vivo, and enhanced deconvolution by computational methods. J Comb Chem 10:3–19. doi:10.1021/cc7001205
Pinilla C, Appel JR, Borras E, Houghten RA (2003) Advances in the use of synthetic combinatorial chemistry: mixture-based libraries. Nat Med 9:118–122. doi:10.1038/70946
Armishaw CJ, Singh N, Medina-Franco JL, Clark RJ, Scott KC, Houghten RA, Jensen AA (2010) A synthetic combinatorial strategy for developing alpha-conotoxin analogs as potent alpha7 nicotinic acetylcholine receptor antagonists. J Biol Chem 285:1809–1821. doi:10.1074/jbc.M109.071183
Reilley KJ, Giulianotti M, Dooley CT, Nefzi A, McLaughlin JP, Houghten RA (2010) Identification of two novel, potent, low-liability antinociceptive compounds from the direct in vivo screening of a large mixture-based combinatorial library. AAPS J 12:318–329. doi:10.1208/s12248-010-9191-3
Yongye AB, Appel JR, Giulianotti MA, Dooley CT, Medina-Franco JL, Nefzi A, Houghten RA, Martinez-Mayorga K (2009) Identification, structure–activity relationships and molecular modeling of potent triamine and piperazine opioid ligands. Biorg Med Chem 17:5583–5597. doi:10.1016/j.bmc.2009.06.026
Hemmer B, Gran B, Zhao YD, Marques A, Pascal J, Tzou A, Kondo T, Cortese I, Bielekova B, Straus SE, McFarland HF, Houghten R, Simon R, Pinilla C, Martin R (1999) Identification of candidate T-cell epitopes and molecular mimics in chronic Lyme disease. Nat Med 5:1375–1382. doi:10.1002/0471142735.im0905s45
Zhao Y, Gran B, Pinilla C, Markovic-Plese S, Hemmer B, Tzuo A, Whitney LW, Biddison WE, Martin R, Simon R (2001) Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J Immunol 167:2130–2141
Dooley CT, Chung NN, Wilkes BC, Schiller PW, Bidlack JM, Pasternak GW, Houghten RA (1994) An all D-amino-acid opioid peptide with central analgesic activity from a combinatorial library. Science 266:2019–2022. doi:10.1126/science.7801131
Houghten RA, Dooley CT, Appel JR (2006) In vitro and direct in vivo testing of mixture-based combinatorial libraries for the identification of highly active and specific opiate ligands. AAPS J 8:E371–E382. doi:10.1208/aapsj080242
Venhorst J, ter Laak AM, Commandeur JN, Funae Y, Hiroi T, Vermeulen NP (2003) Homology modeling of rat and human cytochrome P450 2D (CYP2D) isoforms and computational rationalization of experimental ligand-binding specificities. J Med Chem 46:74–86. doi:10.1021/jm0209578
Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32:335–373. doi:10.1186/1471-2105-10-58
Martin YC (1992) 3D database searching in drug desing. J Med Chem 35:2145–2154. doi:10.1021/jm00090a001
Boehm M, Wu T-Y, Claussen H, Lemmen C (2008) Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces. J Med Chem 51:2468–2480. doi:10.1021/jm0707727
Medina-Franco JL, Maggiora GM, Giulianotti MA, Pinilla C, Houghten RA (2007) A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem Biol Drug Desig 70:393–412. doi:10.1111/j.1747-0285.2007.00579.x
Maggiora GM (2006) On outliers and activity cliffs: why QSAR often disappoints. J Chem Inf Model 46:1535–1535. doi:10.1021/ci060117s
Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov 1:882. doi:10.1038/nrd941
Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York
Nikolova N, Jaworska J (2003) Approaches to measure chemical similarity—a review. QSAR Comb Sci 22:1006–1026. doi:10.1186/1471-2121-8-S1-S6
Medina-Franco JL, Martinez-Mayorga K, Bender A, Mari’n RM, Giulianotti MA, Pinilla C, Houghten RA (2009) Characterization of activity landscapes using 2D and 3D similarity methods: consensus activity cliffs. J Chem Inf Model 49:477–491. doi:10.1021/ci800379q
Martinez-Mayorga K, Medina-Franco JL, Giulianotti MA, Pinilla C, Dooley CT, Appel JR, Houghten RA (2008) Conformation–opioid activity relationships of bicyclic guanidines from 3D similarity analysis. Bioorg Med Chem 16:5932–5938. doi:10.1016/j.bmc.2008.04.061
Smith JAM, Hunter JC, Hill RG, Hughes J (1989) A kinetic analysis of κ-opioid agonist binding using the selective radioligand [3H]U69593. J Neurochem 53:27–36. doi:10.1111/j.1471-4159.1989.tb07291.x
Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M (2004) WOMBAT: world of molecular bioactivity. In: Oprea TI (ed) Chemoinformatics in drug discovery. Wiley-VCH, New York, pp 223–239
OpenEye Scientific Software (2007) ROCS v.2.3.1. OpenEye Scientific Software, Santa Fe (see http://www.eyesopen.com)
OpenEye Scientific Software (2007) OMEGA v.2.2.1. OpenEye Scientific Software, Santa Fe (http://www.eyesopen.com)
Ostresh JM, Schoner CC, Hamashin VT, Nefzi A, Meyer JP, Houghten RA (1998) Solid-phase synthesis of trisubstituted bicyclic guanidines via cyclization of reduced N-acylated dipeptides. J Org Chem 63:8622–8623. doi:10.1208/aapsj080242
Sykes MJ, McKinnon RA, Miners JO (2008) Prediction of metabolism by cytochrome P4502C9: alignment and docking studies of a validated database of substrates. J Med Chem 51:780–791. doi:10.1021/jm7009793
Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L (2005) The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 38:404–415. doi:10.1186/1471-2105-8-331
Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48:2534–2547. doi:10.1021/ci800101j
Shanmugasundaram V, Maggiora GM (2001) Characterizing property and activity landscapes using an information-theoretic approach. Abstr Pap Am Chem Soc 222:32-CINF
Patterson DE, Cramer RD, Ferguson AM, Clark RD, Weinberger LE (1996) Neighborhood behavior: a useful concept for validation of “molecular diversity” descriptors. J Med Chem 39:3049–3059. doi:10.1021/ci025635r
Whittle M, Gillet VJ, Willett P, Loesel J (2006) Analysis of data fusion methods in virtual screening: similarity and group fusion. J Chem Inf Model 46:2206–2219. doi:10.1016/S1367-5931(00)00110-1
Whittle M, Gillet VJ, Willett P, Loesel J (2006) Analysis of data fusion methods in virtual screening: theoretical model. J Chem Inf Model 46:2193–2205. doi:10.1016/S1367-5931(00)00110-1