Systematic generation of chemical structures for rational drug design based on QSAR models

Current Computer-Aided Drug Design

Volumn 7, Issue 1, 2011, Pages 1-9

  Funatsu, Kimito ^a   Miyao, Tomoyuki ^a   Arakawa, Masamoto ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Applicability domain; Chemical space; Drug design; Lead generation; Molecular design; Qsar; Qspr; Structure generation

Indexed keywords

BIOACTIVITY; BIOINFORMATICS; COMPUTATIONAL CHEMISTRY; DIGITAL LIBRARIES; MOLECULAR GRAPHICS; STRUCTURAL DESIGN;

APPLICABILITY DOMAIN; CHEMICAL SPACE; DRUG DESIGN; GENERATION METHOD; LEAD GENERATION; MOLECULAR DESIGN; QSAR; QSPR; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODELING; STRUCTURE GENERATION;

LEAD COMPOUNDS;

DRUG; NATURAL PRODUCT;

ARTICLE; BIOLOGICAL ACTIVITY; CHEMICAL STRUCTURE; COMPUTATIONAL FLUID DYNAMICS; DATA BASE; DRUG DESIGN; DRUG SCREENING; EVOLUTIONARY ALGORITHM; LIBRARY; MACHINE LEARNING; MOLECULAR DOCKING; QUANTITATIVE ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SIMULATION; VIRTUAL REALITY;

EID: 78951489458     PISSN: 15734099     EISSN: None     Source Type: Journal    
DOI: 10.2174/157340911793743556     Document Type: Article

References (102)

1. Macarron, R. Critical review of the role of HTS in drug discovery. Drug Discov. Today, 2006, 11, 277-279.
2. Masimirembwa, C.M.; Thompson, R.; Andersson, T.B. In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery. Comb. Chem. High Throughput Screen., 2001, 4, 245-263.
3. Feher, M.; Schmidt, J.M. Property distributions: differences between drugs, natural products and molecules from combinatorial chemistry. J. Chem. Inf. Comput. Sci., 2003, 43, 218-227.
4. Bohacek, R.S.; McMartin, C.; Guida, W.C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev., 1996, 16, 3-50.
5. Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath J. Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discov., 2004, 3, 935-949.
6. Kuntz, I.D.; Meng, E.C.; Shoichet, B.K. Structure-based molecular design. Acc. Chem. Res., 1994, 27, 117-123.
7. Sasaki, S.; Kudo, Y.; Ochiai, S.; Abe, H. Automated chemical structure analysis of organic compounds - attempt to structure determination by use of NMR. Mikrochim. Acta., 1971, 59, 726-742.
8. Masinter, L.M.; Sridharan, N.S.; Carhart, R.E.; Smith, D.H. Applications of artificial intelligence for chemical inference. 12. Exhaustive generation of cyclic and acyclic isomers. J. Am. Chem. Soc., 1974, 96, 7702-7714.
9. Shelley, C.A.; Hays, T.R.; Munk, M.E.; Roman, R.V. Approach to automated partial structure expansion. Anal. Chim. Acta., 1978, 103, 121-132.
10. 13C NMR rule formation. Org. Magn. Reson., 1978, 11, 378-384.
11. Carhart, R.E.; Smith, D.H.; Gray, N.A.B.; Nourse, J.G.; Djerassi, C.J. Applications of artificial-intelligence for chemical inference. 37. GENOA - a computer-program for structure elucidation utilizing overlapping and alternative substructures. Org. Chem., 1981, 46, 1708-1718.
12. Christie, B.D.; Munk, M.E. The application of two-dimensional nuclear magnetic resonance spectroscopy in computer-assisted structure elucidation. Anal. Chim. Acta., 1987, 200, 347-361.
13. Funatsu, K.; Miyabayaski, N.; Sasaki, S. Further development of structure generation in the automated structure elucidation system CHEMICS. J. Chem. Inf. Comput. Sci., 1988, 28, 18-28.
14. Luinge, H.J.; Van Der Mass, J.H. AEGIS, an algorithm for the exhaustive generation of irredundant structures. Chemom. Intell. Lab. Syst., 1990, 8, 157-165.
15. Faulon, J. L. Stochastic generator of chemical structure. 1. Application to the structure elucidation of large molecules. J. Chem. Inf. Comput. Sci., 1994, 34, 1204-1218.
16. Munk, M.E. Computer based structure determination: then and now. J. Chem. Inf. Compt. Sci., 1998, 38, 997-1009.
17. Gray N.A.B. Computer-Assisted Structure Elucidation, Wiley: New York, 1986.
18. Benecke, C.; Grund, R.; Hohberger, R.; Kerber, A.; Laue, R.; Wieland, T. MOLGEN+, a generator of connectivity isomers and stereoisomers for molecular structure elucidation. Anal. Chim. Acta., 1995, 314, 141-147.
19. Wieland, T.; Kerber, A.; Laue, R. Principles of the generation of constitutional and configurational isomers. J. Chem. Inf. Comput. Sci., 1996, 36, 431-419
20. Contreras, M.L.; Valdivia, R.; Rozas, R. Exhaustive generation of organic isomers. 1. Acyclic structures. J. Chem. Inf. Comput. Sci., 1992, 32, 323-330.
21. Contreras, M.L.; Valdivia, R.; Rozas, R. Exhaustive generation of organic isomers. 2. Cyclic structures: new compact molecular code. J. Chem. Inf. Comput. Sci., 1992, 32, 481-491.
22. Contreras, M.L.; Rozas, R.; Valdivia, R. Exhaustive generation of organic isomers. 3. Acyclic, cyclic, and mixed compounds. J. Chem. Inf. Comput. Sci., 1994, 34, 610-616.
23. Contreras, M.L.; Rozas, R.; Valdivia, R.; Agüero, R. Exhaustive generation of organic isomers. 4. Acyclic stereoisomers with one or more chiral carbon atoms. J. Chem. Inf. Comput. Sci., 1995, 35, 752-758.
24. Contreras, M.L.; Alvarez, J.; Guajardo, D.; Rozas, R. Algorithm for exhaustive and nonredundant organic stereoisomer generation. J. Chem. Inf. Model., 2006, 46, 2288-2298.
25. Pollock, S.N.; Coutsias, E.A.; Wester, M.J.; Oprea, T.I. Scaffold topologies. 1. Exhaustive enumeration up to eight rings. J. Chem. Inf. Model., 2008, 48, 1304-1310.
26. Molchanova, M.S.; Shcherbukhin, V.V.; Zefirov, N.S. Computer generation of molecular structures by the SMOG program. J. Chem. Inf. Comput. Sci., 1996, 36, 888-899.
27. Molchanova, M.S.; Zefirov, M.S. Irredundant generation of isomeric molecular structures with some known fragments. J. Chem. Inf. Comput. Sci., 1998, 38, 8-22.
28. Zhu, S.Y.; Zhang, J.P. Exhaustive generation of structural isomers for a given empirical formula - a new algorithm. J. Chem. Inf. Comput. Sci., 1982, 22, 34-38.
29. Bangov, I.P. Structure generation from a gross formula. 7. Graph isomorphism a consequence of the vertex equivalence. J. Chem. Inf. Comput. Sci., 1994, 34, 318-324.
30. Faulon, J.L.; Visco, D.P.; Roe, D. in Reviews in Computational Chemistry; Lipkowitz, K.B.; Larter, R.; Cundari, T.R., Eds.; Wiley, USA, 2005; Vol. 21, pp. 209-286.
31. Melnikov, A.A.; Palyulin, V.A.; Zefirov, N.S. Generation of molecular graphs for QSAR studies: An approach based on supergraphs. J. Chem. Inf. Model., 2007, 47, 2077-2088.
32. Tratch, S.S.; Lomova, O.A.; Sukhachev, D.V.; Palyulin, V.A.; Zefirov, N.S. Generation of molecular graphs for QSAR studies: An approach based on acyclic fragment combinations. J. Chem. Inf. Comput. Sci., 1992, 32, 130-139.
33. Lessel, U.; Wellenzohn, B.; Lilienthal, M.; Claussen, H. Searching fragment spaces with feature trees. J. Chem. Inf. Model., 2009, 49, 270-279.
34. Wong, W.W.L.; Burkowski, F.J. A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem. J. Cheminformat., 2009, 1, 4.
35. Pären, J.; Degen, J.; Rarey, M. Exploring fragment spaces under multiple physicochemical constraints. J. Comput.-Aided Mol. Des., 2007, 21, 327-340.
36. Fink, T.; Bruggesser, H.; Reymond, J.L. Virtual exploration of the small molecule chemical universe below 160 daltons. Angew Chem., Int. Ed., 2005, 44, 1504-1508.
37. Fink, T.; Reymond, J.L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: Assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model., 2007, 47, 342-353.
38. Hert, J.; Irwin, J.J.; Laggner, C.; Keiser, M.J.; Shoichet, B.K. Quantifying biogenic bias in screening libraries. Nat. Chem. Biol., 2009, 5, 479-483.
39. Wester, M.J.; Pollock, S.N.; Coutsias, E.A.; Allu, T.K.; Muresan, S.; Oprea T.I. Scaffold topologies. 2. Analysis of chemical databases. J. Chem. Inf. Model., 2008, 48, 1311-1324.
40. Nguyen, K.T.; Syed, S.; Urwyler, S.; Bertrand, S.; Bertrand, D.; Reymond, J.L. Discovery of NMDA glycine site inhibitors from the chemical universe database GDB. Chem. Med. Chem., 2008, 3 1520 -1524.
41. Blum, L.C.; Reymond, J.L. 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc., 2009, 131, 8732-8733.
42. Faulon, J.L. Stochastic generator of chemical structure. 2. Using simulated annealing to search the space of constitutional isomers. J. Chem. Inf. Comput. Sci., 1996, 36, 731-740.
43. Kvasnička, V.; Pospíchal, J. Simulated annealing construction of molecular graphs with required properties. J. Chem. Inf. Comput. Sci., 1996, 36, 516-526.
44. Kutchukian, P.S.; Lou, D.; Shakhnovich, E.I. FOG: fragment optimized growth algorithm for the de novo generation of molecules. J. Chem. Inf. Model., 2009, 49, 1630-1642.
45. Venkatasubramanian, V.; Chan, K.; Caruthers, J.M. Evolutionary design of molecules with desired properties using the genetic algorithm. J. Chem. Int. Comput. Sci., 1995, 35, 188-195.
46. Globus, A.; Lawton, J.; Wipke, T. Automatic molecular design using evolutionary techniques. Nanotechnology, 1999, 10, 290-299.
47. Nachbar, R. B. Molecular evolution: Automated manipulation of hierarchical chemical topology and its application to average molecular structures. Genetic Programm. Evolvable Machines, 2000, 1, 57-94.
48. Kamphausen, S; Höltge, N; Wirsching, F; Morys-Wortmann, C; Riester, D.; Goetz, R; Thürk, M; Schwienhorst, A. Genetic algorithm for the design of molecules with desired properties. J. Comput.-Aided Mol. Des., 2002, 16, 551-567.
49. Hecht, D.; Fogel, G.B. Novel in silico approach to drug discovery via computational intelligence. J. Chem. Inf. Model., 2009, 49, 1105-1121.
50. Jorgensen, W.L.; Ruiz, C.L.; Rives, T.J.; Basavapathruni, A.; Anderson, K.S.; Hamiltona, A.D. Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg. Med. Chem. Lett., 2006, 16, 663-667.
51. Honma, T. Recent advances in de novo design strategy for practical lead identification. Med. Res. Rev., 2003, 23, 606-632.
52. Schneider, G.; Fechner, U. Computer based de novo design of drug like molecules. Nat. Rev. Drug Discov., 2005, 4, 649-663.
53. Mauser, H.; Guba, W. Recent developments in de novo design and scaffold hopping. Curr. Opin. Drug Discov. Devel., 2008, 11, 365-374.
54. Brown, N.; McKay, B.; Gilardoni, F.; Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci., 2004, 44, 1079-1087.
55. Brown, N.; McKay, B.; Gasteiger, J. The de novo design of median molecules within a property range of interest. J. Comput.-Aided Mol. Des., 2004, 18, 761-771.
56. Brown, N.; McKay, B.; Gasteiger, J. A novel workflow for the inverse QSPR problem using multiobjective optimization. J. Comput. Aided Mol. Des., 2006, 20, 333-341.
57. Deursen, R.; Reymond, J.L. Chemical space travel. ChemMedChem., 2007, 2, 636-640.
58. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors, Wiley-VCH: Weinheim 2000.
59. Cramer III, R.D.; Patterson, A.E.; Bunce, J.D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967.
60. Klebe, G. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem., 1994, 37, 4130-4146.
61. Todeschini, R.; Gramatica, P. SD-modelling and prediction by WHIM descriptors. part 5. theory development and chemical meaning of WHIM descriptors. Quant. Struct.-Act. Relat., 1997, 16, 113-119.
62. Arakawa, M.; Hasegawa, K.; Funatsu, K. Novel alignment method of small molecules using Hopfield neural network. J. Chem. Inf. Comput. Sci., 2003, 43, 1390-1395.
63. Arakawa, M.; Hasegawa, K.; Funatsu, K. Application of novel molecular alignment method using the Hopfield neural network to 3D-QSAR. J. Chem. Inf. Comput. Sci., 2003, 43, 1396-1402.
64. Arakawa, M.; Hasegawa, K.; Funatsu, K. Tailored scoring function of trypsin-benzamidine complex using COMBINE descriptors and support vector regression. Chemom. Intell. Lab. Syst., 2008, 92, 145-151.
65. Mei, H.; Zhou, Y.; Liang, G.Z.; Li, Z.L. Support vector machine applied in QSAR modeling. Chinese Sci. Bull., 2005, 50, 2291-2296.
66. Wang, Y.H.; Li, Y.; Wang, B. An in silico method for screening nicotine derivatives as cytochrome P450 2A6 selective inhibitors based on kernel partial least squares. Int. J. Mol. Sci., 2007, 8, 166-179.
67. Todeschini, R.; Consonni, V; Mauri, A; Pavan, M. Detecting "bad" regression models: multicriteria fitness functions in regression analysis. Anal. Chim. Acta, 2004, 515, 199-208.
68. Kier, L.B.; Hall, L.H. The generation of molecular structures from a graph-based QSAR equation. Quant. Struct.-Act. Relat., 1993, 12, 383-388.
69. Skvortsova, M.I.; Baskin, I.I.; Slovokhotova, O.L.; Palyulin, V.A.; Zefirov, N.S. Inverse problem in QSAR/QSPR studies for the case of topological indices characterizing molecular shape (Kier indices). J. Chem. Inf. Comput. Sci., 1993, 33, 630-634.
70. Kier, L.B. A shape index from molecular graphs. Quant. Struct.-Act. Relat., 1985, 4, 109-116.
71. Visco, D.P.; Pophale, R.S.; Rintoul, M.D.; Faulon, J.L. Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor. J. Mol. Graphics Model., 2002, 20, 429-438.
72. Faulon, J.L.; Churchwell, C.J. The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences. J. Chem. Inf. Comput. Sci., 2003, 43, 721-734.
73. Faulon, J.L. The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J. Chem. Inf. Comput. Sci., 2003, 43, 707-720.
74. Faulon, J.L. On using graph-equivalent classes for the structure elucidation of large molecules. J. Chem. Inf. Comput. Sci., 1992, 32, 338-348.
75. Churchwell, C.J.; Rintoul, M.D.; Martin, S.; Visco, D.P.; Kotu, A.; Larson, R.S.; Sillerud, L.O.; Brown, D.C.; Faulon, J.L. The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides. J. Mol. Graphics Model., 2004, 22, 263-273.
76. Weis, D.C.; Faulon, J.L.; LeBorne, R.C.; Visco, D.P. The signature molecular descriptor. 5. The design of hydrofluoroether foam blowing agents using inverse-QSAR. Ind. Eng. Chem. Res., 2005, 44, 8883-8891.
77. Miyao, T.; Arakawa, M.; Funatsu, K. Exhaustive structure generation for inverse-QSPR/QSAR. Mol. Inform., 2010, 29, 111-125.
78. McKay, B.D. Isomorph-free exhaustive generation. J. Algor., 1998, 26, 306-324.
79. Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22, 69-77.
80. Sheridan, R.P.; Feuston, B.P.; Maiorov, V.N.; Kearsley, S.K. Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J. Chem. Inf. Comput. Sci., 2004, 44, 1912-1928.
81. Schroeter, T.S.; Schwaighofer, A.; Mika, S.; Laak, A.T.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Müller, K.R. Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J. Comput. Aided Mol. Des., 2007, 21, 651-664.
82. Weaver, S.; Gleeson, M.P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graphics Model., 2008, 26, 1315-1326.
83. Zhu, H.; Tropsha, A.; Fourches, D.; Varnek, A.; Papa, E.; Gramatica, P.; Öberg, T.; Dao, P.; Cherkasov, A.; Tetko, I.V. Combinatorial QSAR modeling of chemical toxicants tested against tetrahymena pyriformis. J. Chem. Inf. Model., 2008, 48, 766-784.
84. Horvath, D.; Marcou, G.; Varnek, A. Predicting the predictability: A unified approach to the applicability domain problem of QSAR models. J. Chem. Inf. Model., 2009, 49, 1762-1776.
85. Tetko, I.V.; Sushko, I.; Pandey, A.K.; Zhu, H.; Tropsha, A.; Papa, E.; Öberg, T.; Todeschini, R.; Fourches, D.; Varnek A. Critical assessment of QSAR models of environmental toxicity against tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model., 2008, 48, 1733-1746.
86. Zhang, L.; Garcia-Munoz, S. A comparison of different methods to estimate prediction uncertainty using partial least squares: A practitioner's perspective. Chemom. Intell. Lab. Syst., 2009, 97, 152-158.
87. Dinadayalane, T.C.; Priyakumar, U.D.; Sastry, G.N. Exploration of C6H6 potential energy surface: A computational effort to unravel the relative stabilities and synthetic feasibility of new benzene isomers. J. Phys. Chem. A, 2004, 108, 11433-11448.
88. Hoffmann, R.; Hopf, H. Learning from molecules in distress. Angew. Chem. Int. Ed., 2008, 47, 4474-4481.
89. Nikoli, S.; Toli, N.T.M. Complexity of molecules. J. Chem. Inf. Comput. Sci., 2000, 40, 920-926.
90. Paton, R.S.; Goodman, J.M. Exploration of the accessible chemical space of acyclic alkanes. J. Chem. Inf. Model., 2007, 47, 2124-2132.
91. Ertl, P.; Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminf., 2009, 1, 8.
92. Boda, K.; Seidel, T.; Gasteiger, J. Structure and reaction based evaluation of synthetic accessibility. J. Comput. Aided Mol. Des., 2007, 21, 311-325.
93. Gasteiger, J. De novo design and synthetic accessibility, J. Comput. Aided Mol. Des., 2007, 21, 307-309.
94. Baber, J.C.; Feher, M. Predicting synthetic accessibility: Application in drug discovery and development. Mini. Rev. Med. Chem., 2004, 4, 681-692.
95. Lajiness, M.S.; Maggiora, G.M.; Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem., 2004, 47, 4891-4896.
96. Zheng, S.; Luo, X.; Chen, G.; Zhu, W.; Shen, J.; Chen, K.; Jiang, H. A new rapid and effective chemistry space filter in recognizing a druglike database. J. Chem. Inf. Model., 2005, 45, 856-862.
97. Biswas, D.; Roy, S.; Sen, S. A simple approach for indexing the oral druglikeness of a compound: discriminating druglike compounds from nondruglike ones. J. Chem. Inf. Model., 2006, 46, 1394-1401.
98. Takaoka, Y.; Endo, Y.; Yamanobe, S.; Kakinuma, H.; Okubo, T.; Shimazaki, Y.; Ota, T.; Sumiya, S.; Yoshikawa, K. Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition. J. Chem. Inf. Comput. Sci., 2003, 43, 1269-1275.
99. Schneider, N.; Jackels, C.; Andres, C.; Hutter, M.C. Gradual in silico filtering for druglike substances. J. Chem. Inf. Model., 2008, 48, 613-628.
100. Fox, T.; Kriegl, J.M. Machine learning techniques for in silico modeling of drug metabolism. Curr. Top. Med. Chem., 2006, 6, 1579-1591.
101. Gombar, V.K.; Alberts, J.J.; Cassidy, K.C.; Mattioni, B.E.; Mohutsky, M.A. In silico metabolism studies in drug discovery: prediction of metabolic stability. J. Comput.-Aided Drug Des., 2006, 2, 177-188.
102. Schwaighofer, A.; Schroeter, T.; Mika, S.; Hansen, K.; Laak, A.; Lienau, P.; Reichel, A.; Heinrich, N.; Mülle, K.R. A probabilistic approach to classifying metabolic stability. J. Chem. Inf. Model., 2008, 48, 785-796