Abstract
The design of novel, pharmaceutically relevant compounds complementary to a given target binding site has long been considered as the “holy grail” in drug design.
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References
Bailey D, Brown D (2001) High-throughput chemistry and structure-based design: survival of the smartest. Drug Discov Today 6: 57–59
Blundell T, Jhoti H, Abell C (2002) High-throughput crystallography for lead discovery in drug design. Nat Rev Drug Discov 1: 45–54
Bohacek RS, McMartin C (1994) Multiple highly diverse structures complementary to enzyme binding sites: results of extensive application of a de novo design method incorporating combinatorial growth. J Am Chem Soc 116: 5560–5571
Böhm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6: 61–78
Böhm HJ, Banner DW, Weber L (1999) Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J Comput Aided Mol Des 13: 51–56
Brady Jr GB, Stouten PFW (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14: 383–401
Bruno U, Cole J, Lommerse J, Rowland RS, Taylor R, Verdonk ML (1997) IsoStar: a library of information about nonbonded interactions. J Comput Aided Mol Des 11: 525–537
Clark DE, Frenkel D, Levy SA, Li J, Murray CW, Robson B, Waszkowycz B, Westhead DR (1995) PRO-LIGAND: an approach to de novo molecular design. 1. Application to the design of organic molecules. J Comput Aided Mol Des 9: 13–32
Clark DE, Firth MA, Murray CW (1996) MOLMAKER: de novo generation of 3D databases for use in drug design. J Chem Inf Comput Sci 36: 137–145
Cramer RD, DePriest S (1993) LEAPFROG module, implemented in the SY- BYL program. Tripos Assoc. St. Louis, MO
Danziger DJ, Dean PM (1989) Automated site-directed drug design: a general algorithm for knowledge acquisition about hydrogen-regions at protein surfaces. Proc R Soc Lond B Biol Sci 236: 101–114
DeWitte RS, Ishchenko AV, Shakhnovich EI (1997) SmoG: de novo design method based on simple, fast, and accurate free energy estimates. 2. Case studies in molecular design. J Am Chem Soc 119: 4608–4617
Eddershaw PJ, Beresford AP, Bayliss MK (2000) ADME/PK as part of a rational approach to drug discovery. Drug Discov Today 5: 409–414
Eisen MB, Wiley DC, Karplus M, Hubbard RE (1994) HOOK: a program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site. Proteins 19: 199–221
Gehlhaar DK, Moerder KE, Zichi D, Sherman CJ, Ogden RC, Freer ST (1995) De novo design of enzyme inhibitors by Monte Carlo ligand generation. J Med Chem 38: 466–472
Gillet V, Newell W, Mata P, Myatt G, Sike S, Zsoldos Z, Johnson AP (1993) SPROUT: a program for structure generation. J Comput Aided Mol Des 7: 127–153
Gillet VJ, Myatt G, Zsoldos Z, Johnson AP (1995) SPROUT, HIPPO and CAESA: tools for de novo structure generation and estimation of synthetic accessibility. Perspect Drug Discov Des 3: 34–50
Goodford PJ (1985) A computational procedure of determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28: 849–857
Grzybowski BA, Ishchenko AV, Kim CY, Topalov G, Chapman R, Christianson DW, Whitesides GM, Shakhnovich EI (2002) Combinatorial computational method gives new picomolar ligands for a known enzyme. Proc Natl Acad Sci USA 99: 1270–1273
Hendlich M, Rippman F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule binding sites in proteins. J Mol Graph Model 15: 359–363
Hirst JD (1998) Predicting ligand-binding energies. Curr Op Drug Disc Dev 1: 28–33
Ho CM, Marshall GR (1993) FOUNDATION: a program to retrieve all possible structures containing a user defined minimum number of matching query elements from three dimensional databases. J Comput Aided Mol Des 7: 623–647
Honma, T, Hayashi K, Aoyama T, Hashimoto N, Machida T, Fukasawa K, Iwama T, Ikeura C, Ikuta M, Suzuki-Takahashi I, Iwasawa Y, Hayama T, Nishimura S, Morishima H (2001) Structure-based generation of a new class of potent cdk4 inhibitors: new de novo design strategy and library design. J Med Chem 44: 4615–4627
Johnson AP (2000) Further development and applications of computer programs for de novo ligand design. Astbury Centre for Structural Molecular Biology, annual report
Kick EK, Roe DC, Skillman AG, Liu G, Ewing TJA, Sun Y, Kuntz ID, Elhnan JA (1997) Structure-based design and combinatorial chemistry yield low nanomolar inhibitors of cathepsin D. Chem Bio! 4: 297–307
Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33: 889–897
Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161: 269–288
Laskowski RA (1995) SURFNET: a program for visualizing surfaces, cavities and intermolecular interactions. J Mol Graph 31: 2735–2748
Lauri G, Bartlett PA (1994) CAVEAT: a program to facilitate the design of organic molecules. J Comput-Aided Mol Des 8: 51–66
Lawrence MC, Davis PC (1992) CLIX: a search algorithm for finding novel ligands capable of binding proteins of known three-dimensional structure. Proteins: Structure, Function, and Genetics 12: 31–41
Leach AR, Bryce RA, Robinson AJ (2000) Synergy between combinatorial chemistry and de novo design. J Mol Graph Model 18: 358–367
Levitt DG, Banaszak LJ (1992) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10: 229–234
Liang J, Edelsbrunner H, Woodward C (1998) Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci 7: 1884–1897
Miranker A, Karplus M (1991) Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11: 29–34
Miranker A, Karplus M (1995) An automated method for dynamic ligand design. Proteins 23: 472–490
Moon J, Howe W (1991) Computer design of bioactive molecules: a method for receptor-based de novo ligand design. Proteins 11: 314–328
Muegge I, Rarey M (2001) Small molecule docking and scoring. In: Lipkowitz KB, Boyd DB (eds) Reviews in computational chemistry, vol. 17. VCH, New York, pp 1–60
Murray CW, Clark DE, Auton TR, Firth MA, Li J, Sykes RA, Waszkowycz B, Westhead DR, Young SC (1997) PRO-SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology. J Comput Aided Mol Des 11: 193–207
Nishibata Y, Itai A (1991) Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 41: 8985–8990
Pearlman DA, Murcko MA (1996) CONCERTS: dynamic connection of frag- ments as an approach to de novo ligand design. J Med Chem 39: 1651–1663
Pegg SCH, Haresco JJ, Kuntz ID (2001) A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des 15: 911–933
Peters KP, Fauck J, Frömmel C (1996) The automatic search for ligand binding sites in proteins of known three-dimensional structure using only geometric criteria. J Mol Biol 256: 201–213
Roe DC, Kuntz ID (1995) BUILDER v.2: improving the chemistry of a de novo design strategy. J Comput-Aided Mol Des 9: 269–282
Rotstein SH, Murcko MA (1993a) GenStar: a method for de novo drug design. J Comput-Aided Mol Des 7: 23–43
Rotstein SH, Murcko MA (1993b) GroupBuild: a fragment based method for de novo drug design. J Med Chem 36: 1700–1710
Schneider G, Böhm HJ (2002) Virtual screening and fast automated docking methods. Drug Discov Today 7: 64–70
Schneider G, Schrödl W, Wallukat G, Nissen E, Rönspeck G, Müller J, Wrede P, Kunze R (1998) Peptide design by artificial neural networks and computer-based evolutionary search. Proc Natl Acad Sci USA 95: 12179–12184
Schneider G, Clement-Chomienne O, Hilfiger L, Schneider P, Kirsch S, Böhm HJ, Neidhart W (2000) Virtual screening for bioactive molecules by evolutionary de novo design. Angew Chem Int Ed Engl 39: 4130–4133
Stahl M, Rarey M (2001) Detailed analysis of scoring functions for virtual screening. J Med Chem 44: 1035–1042
Tame JRH (1999) Scoring functions: a view from the bench. J Comput Aided Mol Des 13: 99–108
Todorov NP, Dean PM (1997) Evaluation of a method for controlling molecular scaffold diversity in de novo ligand design. J Comput Aided Mol Des 11: 175–192
Tschinke V, Cohen NC (1993) The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses. J Med Chem 36: 3863–3870
Verdonk ML, Cole J, Taylor R (1999) SuperStar: a knowledge-based approach for identifying interaction sites in proteins. J Mol Biol 289: 1093–1108
Verlinde CLMJ, Hol WGJ (1994) Structure-based drug design: progress, results and challenges. Structure 2: 577–587
Bless G, Urmann M, Sickenberger B (2001) Medicinal chemistry: challenges and opportunities. Angew Chem Int Ed Engl 40: 3341–3350
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Briem, H. (2003). De Novo Design Methods. In: Waldmann, H., Koppitz, M. (eds) Small Molecule — Protein Interactions. Ernst Schering Research Foundation Workshop, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05314-0_10
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DOI: https://doi.org/10.1007/978-3-662-05314-0_10
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