Abstract
Computer-aided drug design (CADD) now plays an instrumental role in the design and discovery of new therapeutic agents. The general goal of computational drug discovery programs is to accelerate the identification of molecular entities with the desired effect in the human body and to determine the quality, safety, and clinical efficacy of compounds. In this chapter, an overview of computational methods is provided that forms the basis of modern day drug design.
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References
Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioform 10:579–591
Rigden DJ, Fernández-Suárez XM, Galperin MY (2016) The 2016 database issue of Nucleic Acids Research and an updated molecular biology database collection. Nucleic Acids Res 44(D1):D1–D6
Brown RD, Hassan M, Waldman M (2000) Combinatorial library design for diversity, cost efficiency, and drug-like character. J Mol Graph Model 18:427–437
Mizuguchi K (2004) Fold recognition for drug discovery. Drug Discov Today: Targets 3:18–23
Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170
Lyne PD (2002) Structure-based virtual screening: an overview. Drug Discov Today 7:1047–1055
Johnson M, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York
Kubinyi H (1999) Chance favors the prepared mind-from serendipity to rational drug design. J Recept Signal Transduct Res 19:15–39
Song CM, Bernardo PH, Chai CL, Tong JC (2009) CLEVER: pipeline for designing in silico chemical libraries. J Mol Graph Model 27:578–583
Weber L (2005) Current status of virtual combinatorial library design. QSAR Comb Sci 24:809–823
Agrafiotis DK, Lobanov VS, Salemme RF (2002) Combinatorial informatics in the post-genomics era. Nat Rev Drug Discov 1:337–346
Leland BA, Christie JG, Nourse DL et al (1997) Managing the combinatorial explosion. J Chem Inf Comput Sci 37:62–70
Congreve M, Murray CW, Blundell TL (2005) Structural biology and drug discovery. Drug Discov Today 10:895–907
Altschul SF, Madden TL, Schäffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids res 25:3389–3402
Rice DW, Eisenberg D (1997) A 3D-1D substitution matrix for protein fold recognition that includes predicted secondary structure of the sequence. J Mol Biol 267:1026–1038
Conte LL, Ailey B, Hubbard TJP et al (2000) SCOP: a structural classification of proteins database. Nucleic Acids Res 28:257–259
Jones DT, Taylor WR, Thornton JM (1992) A new approach to protein fold recognition. Nature 358:86–89
Miyazawa S, Jernigan RL (1985) Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18:534–552
Betancourt MR, Thirumalai D (1999) Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 8:361–369
Weisel M, Proschak E, Schneider G (2007) PocketPicker: analysis of ligand binding-sites with shape descriptors. Chem Cent J 1:7
Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinf 10:168
Levitt D, Banaszak L (1992) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10:229–234
Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15:359–363
Laskowski R (1995) SURFNET: a program for visualizing molecular surfaces, cavities and intermolecular interactions. J Mol Graph 13:323–330
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
Brady G, Stouten P (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14:383–401
Huang B, Schroeder M (2006) LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct Biol 6:19
Taylor RD, Jewsbury PJ, Essex JW (2002) A review of protein-small molecule docking methods. J Comput Aided Mol Des 16:151–166
Kramer B, Rarey M, Lengauer T (1999) Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 37:228–241
Claussen H, Buning C, Rarey M, Lengauer T (2001) FlexE: efficient molecular docking considering protein structure variations. J Mol Biol 308:377–395
Ewing TJ, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15:411–428
Abagyan R, Totrov M (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol 235:983–1002
Bottegoni G, Kufareva I, Totrov M, Abagyan R (2009) Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking. J Med Chem 52:397–406
Polgár T, Keserü GM (2006) Ensemble docking into flexible active sites. Critical evaluation of FlexE against JNK-3 and beta-secretase. J Chem Inf Model 46:1795–1805
Lorber DM, Shoichet BK (1998) Flexible ligand docking using conformational ensembles. Protein Sci 7:938–950
Morris GM, Goodsel DS, Halliday RS et al (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem 19:1639–1662
Kellenberger E, Rodrigo J, Muller P et al (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57:225–242
Fernández-Recio J, Totrov M, Abagyan R (2004) Identification of protein-protein interaction sites from docking energy landscapes. J Mol Biol 335:843–865
Liu Q, Kwoh CK, Li J (2013) Binding affinity prediction for protein-ligand complexes based on β contacts and B factor. J Chem Inf Model 53:3076–3085
Böhm HJ (1994) The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 8:243–256
Head RD, Smythe ML, Oprea TI et al (1996) VALIDATE: a new method for the receptor-based prediction of binding affinities of novel ligands. J Am Chem Soc 118:3959–3969
Muegge I (2000) A knowledge-based scoring function for protein-ligand interactions: probing the reference state. Perspect Drug Discov Des 20:99–114
Gohkle H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356
Mooij WT, Verdonk ML (2005) General and targeted statistical potentials for protein-ligand interactions. Proteins 61:272–287
Morris GM, Goodsell DS, Huey R, Olson AJ (1996) Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J Comput Aided Mol Des 10:293–304
Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245:43–53
Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM: a program for macromolecular energy, minimization, and dynamics calculation. J Comput Chem 4:187–217
Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov Today 2:436–444
Gardiner SJ, Begg EJ (2006) Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol Rev 58:521–590
Palm K, Stenberg P, Luthman K et al (1997) Polar molecular surface properties predict the intestinal absorption of drugs in humans. Pharm Res 14:568–571
Lipinski CA (2000) Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol 44:235–249
Wenlock MC, Austin RP, Barton P et al (2003) A comparison of physicochemical property profiles of development and marketed oral drugs. J Med Chem 46:1250–1256
Congreve M, Carr R, Murray C et al (2003) A ‘rule of three’ for fragment-based lead discovery? Drug Discov Today 8:876–877
Hou T, Wang J, Zhang W et al (2006) Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem 13:2653–2667
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Tong, J.C. (2017). Applications of Computer-Aided Drug Design. In: Grover, A. (eds) Drug Design: Principles and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-5187-6_1
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DOI: https://doi.org/10.1007/978-981-10-5187-6_1
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