Combining ligand-based and structure-based drug design in the virtual screening arena

Expert Opinion on Drug Discovery

Volumn 2, Issue 1, 2007, Pages 37-49

  Moro, Stefano ^a   Bacilieri, Magdalena ^a   Deflorian, Francesca ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Drug discovery; Integrated computational approaches; Ligand based virtual screening; Structure based virtual screening

Indexed keywords

LIGAND;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BINDING AFFINITY; BINDING SITE; COMPARATIVE MOLECULAR FIELD ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; COMPUTER MODEL; COMPUTER SYSTEM; CRYSTALLOGRAPHY; DRUG DESIGN; DRUG PROTEIN BINDING; DRUG RESEARCH; DRUG TARGETING; HIGH THROUGHPUT SCREENING; MOLECULAR DOCKING; MOLECULAR RECOGNITION; MULTIPLE REGRESSION; PHARMACOPHORE; PRIORITY JOURNAL; PROTEIN BINDING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; VIRTUAL REALITY;

EID: 34447550081     PISSN: 17460441     EISSN: None     Source Type: Journal    
DOI: 10.1517/17460441.2.1.37     Document Type: Review

References (103)

1. GOLEBIOWSKI A, KLOPFENSTEIN SR, PORTLOCK DE: Lead compounds discovered from libraries: Part 2. Curr. Opin. Chem. Biol. (2003) 7:308-325.
2. HERTZBERG RP, POPE AJ: High-throughput screening: new technology for the 21st century. Curr. Opin. Chem. Biol. (2000) 4:445-451.
3. DI MASI JA: New drug innovation and pharmaceutical industry structure: trends in the output of pharmaceutical firms. Drug Inf. J. (2000) 34(4):1169-1194.
4. BEDDELL CR, GOODFORD PJ, NORRINGTON FE, WILKINSON S, WOOTTON R: Compound designed to fit a site of known structure in human haemoglobin. Br. J. Pharmacol. (1976) 57:201-209.
5. GOODFORD PJ: Drug design by the method of receptor fit. J. Med. Chem. (1984) 27:558-564.
6. ROBERTS N, MARTIN J, KINCHINGTON D et al.: Rational design of peptide-based HIV proteinase inhibitors. Science (1990) 243:358-361.
7. ERICKSON J, NEIDHART DJ, VANDRIE J et al.: Design, activity, and 2.8 A crystal structure of a C2 symmetric inhibitor complexed to HIV-1 protease. Science (1990) 249:527-533.
8. DORSEY BD, LEVIN RB, MCDANIEL SL et al.: L-735,524: the design of a potent and orally bioavailable HIV protease inhibitor. J. Med. Chem. (1994) 37:3443-3451.
9. JAIN AN: Virtual screening in lead discovery and optimization. Curr. Opin. Drug Discov. Devel. (2004) 7:396-403.
10. STAHURA FL, BAJORATH J: New methodologies for ligand-based virtual screening. Curr. Pharm. Des. (2005) 11:1189-1202.
11. XUE L, BAJORATH J: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb. Chem. High Throughput Screen. (2000) 3:363-372.
12. MASON JS, GOOD AC, MARTIN EJ: 3D pharmacophores in drug discovery. Curr. Pharm. Des. (2001) 7:567-597.
13. LANGER T, KROVAT EM: Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr. Opin. Drug Discov. Devel. (2003) 6:370-376.
14. POZZAN A: Molecular descriptors and methods for ligand based virtual high throughput screening in drug discovery. Curr. Pharm. Des. (2006) 12:2099-2110.
15. HOU TJ, XU XJ: Recent development and application of virtual screening in drug discovery: an overview. Curr. Pharm. Des. (2004) 10:1011-1033.
16. HANSH C, LEO A: Exploring QSAR. In: Fundamentals and Applications in Chemistry and Biology. Hansh C, Leo A (Eds), American Chemical Society. Washington, DC, USA (1995).
17. BARBOSA F, HORVATH D: Molecular similarity and property similarity. Curr. Top. Med. Chem. (2004) 4:589-600.
18. HOU T, XU X: Recent development and application of virtual screening in drug discovery: an overview. Curr. Pharm. Des. (2004) 10:1011-1033.
19. WOLD S, SJOSTROM M, ERIKSSON L: Partial least squares projections to latent structures (PLS) in chemistry. In: The Encyclopaedia of Computational Chemistry. von Ragué Schleyer P (Ed.), Wiley & Sons, UK (1998):2006-2022.
20. LEACH AR, GILLET VJ: An Introduction in Chemoinformatics. Leach AR, Gillet VJ (Eds), Kluwer Academic Publishers, The Netherlands (2003).
21. ERIKKSON L, JOHANSSON E, KETTANEH-WOLD N, WOLD S: Multi and Megavariate Data Analysis - Principles and Applications. Erikkson L, Johansson E, Kettaneh-Wold N, Wold S (Eds), Umetrics Academy, Sweden (2001).
22. CATANA C, GAO H, ORRENIUS C, STOUTEN P: Linear and nonlinear methods in modeling the aqueous solubility of organic compounds. J. Med. Chem. (2005) 45:170-176.
23. ROCHE O, SCHNEIDER P, ZUEGGE J et al.: Development of a virtual screening method for identification of frequent hitters in compound libraries. J. Med. Chem. (2000) 45:137-142.
24. ZUEGGE J, FECHNER U, ROCHE O, PARRATT NJ, ENGKVIST O, SCHNEIDER G: A fast virtual screening filter for cytochrom450 3A4 inhibition liability of compound libraries. Quant. Struct. Act. Relat. (2002) 21:249-256.
25. SEEL M, TURNER DB, WILLET P: Effect of parameter variation on the effectiveness of HQSAR analysis. Quant. Struct. Act. Relat. (1999) 18:245-252.
26. ROCHE O, TRUBE G, ZUEGGE J, PFLIMLIN P, ALANINE A, SCHNEIDER G: A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. Chem Bio Chem (2002) 3:455-459.
27. GREENE J, KAHN S, SAVOJ H, SPRAGUE P, TEIG S: Chemical function queries for 3D database search. J. Chem. Comput. Sci. (1994) 34:1297-1308.
28. MCGREGOR MJ, MUSKAL SM: Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Comput. Sci. (1999) 39:569-574.
29. LANGER T, KROVAT EM: Chemical feature-based pharmacophores and virtual library screening for discovery of new leads. Curr. Opin. Drug Discov. Devel. (2003) 6:370-376.
30. CATALYST is available from Accelrys, 9685 Scranton Road, San Diego, CA 92121, USA.
31. DISCO is available from Tripos, Inc., St Louis, MO (USA).
32. GASP is available from Tripos, Inc., St Louis, MO (USA).
33. DIXON SL, SMONDYREV AM, RAO SN: PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chem. Biol. Drug Des. (2006) 67:370-372.
34. PATEL Y, GILLET VJ, BRAVI G, LEACH AR: A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J. Comp. Aided Mol. Des. (2002) 16:653-681.
35. GUENER O, CLEMENT O, KUROGI Y: Pharmacophore modeling and three dimensional database searching for drug design using catalyst. Curr. Med. Chem. (2004) 11:2991-3005.
36. VAN DRIE JH: Pharmacophore discovery - lessons learned. Curr. Pharm. Des. (2003) 9:1649-1664.
37. DROR O, SHULMAN-PELEG A, NUSSINOV R, WOLFSON HJ: Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr. Med. Chem. (2004) 11:71-90.
38. CRAMER RD, PATTERSON DE, BUNCE JD: Comparative Molecular Field Analysis (CoMFA) 1.Effects of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. (1988) 110:5959-5967.
39. KUBINI H: Comparative molecular field analysis (CoMFA). In: The Encyclopaedia of Computational Chemistry. von Ragué Schleyer P (Ed.), Wiley & Sons, UK (1998):448-460.
40. KLEBE G, ABRAHAM U: On the prediction of binding properties of drug molecules by comparative molecular field analysis. J. Med. Chem. (1993) 36:70-80.
41. LEMMEN C, LENGAUER T, KLEBE G: FLEXS: a method for fast flexible ligand superimposition. J. Med. Chem. (1998) 41:4502-4520.
42. MOREAU G, BROTO P: The autocorrelation of a topological structure: a new molecular descriptor. Nouv. J. Chim. (1980) 4:359-360.
43. PASTOR M, CRUCIANI G, MCLAY I, PICKETT S, CLEMENTI S: GRid-Independent Descriptors (GRIND): a novel class of alignment-independent three-dimentional molecular descriptors. J. Med. Chem. (2000) 43:3233-3243.
44. FONTAINE F, PASTOR M, SANZ F: Incorporating molecular shape into the alignment-free G Rid-IndependentDescriptors. J. Med. Chem. (2004) 47:2805-2815.
45. GASTEIGERJ, LI X, RUDOLPH C, SADOVSKI J, ZUPAN J: Representation of molecular electrostatic potential by topological feature maps. J. Am. Chem. Soc. (1994) 116:4608-4620.
46. WAGENER M, SADOVSKI J, GASTEIGER J: Autocorrelation of molecular surface properties for modeling corticosteroid binding globulin and cytosolic Ah receptor activity by neural networks. J. Am. Chem. Soc. (1995) 117:7769-7778.
47. BAUKNECHT H, ZELL A, BAYER H, LEVI P, SADOWSKI J, GASTEIGER J: Locating biologically active compounds in medium-sized heterogeneous datasets by topological autocorrelation vectors: dopamine and benzodiazepine agonists. J. Chem. Inf. Comput. Sci. (1996) 36:1205-1213.
48. MORO S, BACILIERI M, FERRARI C, SPALLUTO G: Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs. Curr. Drug. Disc. Tech. (2005) 2:13-21.
49. MORO S, BACILIERI M, CACCIARI B, SPALLUTO G: Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as new strategy for the prediction of the activity of human A(3) adenosine receptor antagonists. J. Med. Chem. (2005) 48:5698-5704.
50. ZUPAN J, GASTEIGER J: Neural Networks in Chemistry and Drug Design (2nd edn). Zupan J, Gasteiger J (Eds),Wiley-VCH, Weinheim, Germany (1999).
51. GASTEIGER J, TECKENTRUP A, TERFLOTH L, SPYCHER S: Neural networks as data mining tools in drug design. J. Phys. Org. Chem. (2003) 16:232-245.
52. CZERMINSKI R, YARSI A, HARTSOUGH D: Use of Support Vector Machine in pattern classification: application to QSAR studies. Quant. Struct. Act. Relat. (2001) 20:227-240.
53. NORINDER U: Support Vector Machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing (2003) 55:337-346.
54. WARMUTH M, LIAO J, RATSCH G, MATHIESON M, PUTTA S, LAMMEN C: Active learning with Support Vector Machines in the drug discovery process. J. Chem. Comput. Sci. (2003) 43:667-673.
55. BYVATOV E, FECHNER U, SADOWSKY J, SCHNEIDER G: Comparison of Support Vector Machines and artificial neural network system for drug/non-drug dassification. J. Chem. Inf. Comp. Sci. (2003) 43:1882-1889.
56. GHOSE AK, CRIPPEN GM: Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. I. partition coefficients as a measure of hydrophobicity. J. Comput. Chem. (1986) 7:565-577.
57. GHOSE AK, CRIPPEN GM: Atomic physicochemical parameters for three-dimensional- structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci. (1987) 27:21-35.
58. GHOSE AK, PRITCHETT A, CRIPPEN GM: Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. 3. Modeling hydrophobic interaction. J. Comput. Chem. (1988) 9:80-90.
59. SCHNEIDER G, NEIDHART W, GILLER T, SCHMID G: 'Scaffold hopping' by topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int. Ed. (1999) 38:2894-2895.
60. MOE (The Molecular Operating Environment) is available from Chemical Computing Group, Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Canada H3A 2R7.
61. SADOWSKI J, KUBINYI H: A scoring scheme for discriminating between drugs and non-drugs. J. Med. Chem. (1998) 41:3325-3329.
62. AJAY A, WALTERS WP, MURCKO MA: Can we learn to distinguish between 'drug-like' and 'nondrug-like' molecules? J. Med. Chem. (1998) 41:3314-3324.
63. Comprehensive Medicinal Chemistry (CMC) and MACCS-II Drug Data Report (MDDR) are available from Molecular Design Limited: San Leandro, CA (USA).
64. Available Chemicals Directory (ACD) is available from Molecular Design Limited: San Leandro, CA (USA).
65. SCAPIN G: Structural biology and drug discovery. Curr. Phaem. Des. (2006) 12:2087-2097.
66. BREDELM, JACOBYE: Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat. Rev. Genet. (2004) 5:262-275.
67. ABOLA E, KUHN P, EARNEST T, STEVENS RC: Automation of X-ray crystallography. Nat. Struct. Biol. (2000) 7:983-977.
68. KUHN P, WILSON K, PATCH MG, STEVENS RC: The genesis of high-throughput structure-based drug discovery using protein crystallography. Curr. Opin. Struct. Biol. (2002) 6:704-710.
69. STOUT TJ, FOSTER PG, MATTHEWS DJ: High-throughput structural biology in drug discovery: protein kinases. Curr. Pharm. Des. (2004) 10:1069-1082.
70. TICKLE I, SHARFF A, VINKOVIC M, YON J, JHOTI H: High-throughput protein crystallography and drug discovery. Chem. Soc. Rev. (2004) 33:558-565.
71. ANDERSON AC: The process of structure-based drug design. Chem. Biol. (2003) 10:787-797.
72. KITCHEN DB, DECORNEZ H, FURR JR, BAJORATH J: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. (2004) 3:935-949.
73. HONMA T, HAYASHI K, AOYAMA T et al.: Structure-based generation of a new class of potent Cdk4 inhibitors: new de novo design strategy and library design. J. Med. Chem. (2001) 44:4615-4627.
74. JI H, ZHANG W, ZHANG M et al.: Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14alpha-demethylase of fungi. J. Med. Chem. (2003) 46:474-485.
75. BOEHM HJ, BOEHRINGER M, BUR D et al.: Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J. Med. Chem. (2000) 43:2664-2674.
76. LIEBESCHUETZ JW, JONES SD, MORGAN PJ et al.: PRO_SELECT: combining structure-based drug design and array-based chemistry for rapid lead discovery. 2. The development of a series of highly potent and selective Factor Xa inhibitors. J. Med. Chem. (2002) 45:1221-1232.
77. TAN C, WEI L, OTTENSMEYER FP et al.: Structure-based de novo design of ligands using a three-dimensional model of the insulin receptor. Bioorg. Med. Chem. Lett. (2004) 14:1407-1410.
78. SCHMIDT JM, MERCURE J, TREMBLAY GB, PAGE M, KALBAKJI A, FEHER M: De novo design, synthesis, and evaluation of novel nonsteroidal phenanthrene ligands for the estrogen receptor. J. Med. Chem. (2003) 46:1408-1418.
79. PIERCE AC, RAO G, BEMIS GW: BREED: Generating novel inhibitors through hybridization of known ligands. Application to CDK2, P38, and HIV protease. J. Med. Chem. (2004) 47:2768-2775.
80. MURCKO M: Recent advances in ligand design methods. In: Reviews in Computational Chemistry. Lipkowitz KB, Boyd DB (Eds), Wiley-VCH, New York, USA (1997): 1-66.
81. SCHNEIDER G, FECHNER U: Computer-based de novo design of drug-like molecules. Nat. Rev. Drug Discov. (2005) 4:649-663.
82. LLOYD DG, BUENEMANN CL, TODOROV NP, MANALLACK DT, DEAN PM: Scaffold hopping in de novo design. Ligand generation in the absence of receptor information. J. Med. Chem. (2004) 47:493-496.
83. COLE JC, MURRAY CW, NISSINK JW, TAYLOR RD, TAYLOR R: Comparing protein-ligand docking programs is difficult. Proteins (2005) 60:325-332.
84. LEACH AR, SHOICHET BK, PEISHOFF CE: Prediction of protein-ligand interaction. Docking and scoring: successes and gaps. J. Med. Chem. (2006) 49:5851-5855.
85. SOUSA SF, FERNANDES PA, RAMOS MJ: Protein-ligand docking: current status and future challenges. Proteins (2006) 65:15-26.
86. YANG JM, CHEN YF, SHEN TW, KRISTAL BS, HSU DF: Consensus scoring criteria for improving enrichment in virtual screening. J. Chem. Inf. Model. (2005) 45:1134-1146.
87. RODINGER T, POMES R: Enhancing the accuracy, the efficiency and the scope of free energy simulations. Curr. Opin. Struct. Biol. (2005) 15:164-170.
88. AJAY WP, MURCKO MA: Can we learn to distinguish between 'drug-like' and 'non drug-like' molecules? J. Med. Chem. (1998) 41:3314-3324.
89. LIPINSKI CA, LOMBARDO F, DOMINY BW, FEENEY PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. (1997) 23:3-25.
90. MATTER H, BARINGHAUS KH, NAUMANN T, KLABUNDE T, PlRARD B: Computational approaches towards the rational design of drug-like compound libraries. Comb. Chem. High Throughput Screen. (2001) 4:453-475.
91. HERT J, WILLETT P, WILTON DJ: New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J. Chem. Inf. Model. (2006) 46:462-470.
92. LIPINSKI C, HOPKINS A: Navigating chemical space for biology and medicine. Nature (2004) 432:855-861.
93. CHEN YZ, ZHI DG: Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins (2001) 43:217-226.
94. HINDLE SA, RAREY M, BUNING C, LENGAUER T: Flexible docking under pharmacophore type constrains. J. Comput. Aided Mol. Des. (2002) 16:129-149.
95. GOTO J, KATAOKA R, HIRAYAMA N: Ph4DOCK: Pharamcophore-based protein-ligand docking. J. Med. Chem. (2004) 47:6804-6811.
96. MOITESSIER N, HENRY C, MAIGRET B, CHAPLEUR Y: Combining pharmacophore search, automated docking and molecular dynamics simulations as a novel strategy fro flexible docking. Proof of concept: docking of arginine-glycine-aspartatic acid-like compounds into the avb3 binding site. J. Med. Chem. (2004) 47:4178-4187.
97. SIPPL W, HOLTJE HD: Structure-based 3D-QSAR-merging the accuracy of structure-based alignments with the computational efficiently of ligand-based methods. Teochem (2000) 503:31-50.
98. SIPPL W: Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3D-QSAR methods. Bioorg. Med. Chem. (2002) 10:3741-3755.
99. BRIGO A, MUSTATA GI, BRIGGS JM, MORO S: Discovery of HIV-1 integrase inhibitors through a novel combination of ligand and structure-based drug design. Med. Chem. (2005) 1:263-275.
100. 3 adenosine receptor antagonists: pytazolo [4,3-e] 1,2,4-triazolo[1,5-c]pyrimidine derivatives as a key study. J. Med. Chem. (2005) 48:152-162.
101. 3 adenosine receptor antagonists: an update. Curr. Med. Chem. (2006) 13:763-771.
102. TSAI KC, CHEN SY, LIANG PH et al.: Discovery of a novel family of SARS-Co protease inhibitors by virtual screening and 3D-QSAR studies. J. Med. Chem. (2006) 49:3485-3495.
103. MORO S, DEFLORIAN F, BACILIERI M, SPALLUTO G: G-protein-coupled receptors as challenging druggable targets: insights from in silico studies. New J. Chem. (2006) 30:301-308.