Methods and applications of structure based pharmacophores in drug discovery

Current Topics in Medicinal Chemistry

Volumn 13, Issue 9, 2013, Pages 1036-1047

  Pirhadi, Somayeh ^a   Shiri, Fereshteh ^b   Ghasemi, Jahan B ^a  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF ZABOL   (Iran)

Author keywords

Hot spot; Multi target drug design; Parallel screening; QSAR; Shape and excluded volumes; Structure based pharmacophores

Indexed keywords

CYCLIN DEPENDENT KINASE 2; HUMAN IMMUNODEFICIENCY VIRUS PROTEINASE; MOLECULAR SCAFFOLD; PLASMEPSIN II;

COMPUTER PROGRAM; DRUG DESIGN; HIGH THROUGHPUT SCREENING; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MOLECULAR INTERACTION; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PHARMACOPHORE; PHYSICOCHEMICAL MODEL; PROCESS OPTIMIZATION; PROTEIN BINDING; PROTEIN DATA BANK; REVIEW; SIMULATION; STRUCTURE ACTIVITY RELATION; VIRTUAL REALITY; X RAY CRYSTALLOGRAPHY;

DRUG DISCOVERY; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MODELS, MOLECULAR; MOLECULAR STRUCTURE;

EID: 84878782605     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/1568026611313090006     Document Type: Review

References (113)

1. van de Waterbeemd, H.; Gifford, E. ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discovery., 2003, 2, 192-204.
2. P. Ueber den jetzigen Stand der Chemotherapie. Ber. Dtsch. Chem. Ges., 1909, 42, 17-47.
3. Wermuth, C. G.; Ganellin, C. R.; Lindberg, P.; Mitscher L. A. Glossary of Terms Used in Medicinal Chemistry (IUPAC Recommendations 1997. Annu. Rep. Med. Chem., 1998, 33, 385-395.
4. Kurogi, Y.; Güner, O. F. Pharmacophore modeling and threedimensional database searching for drug design using catalyst. Curr. Med. Chem., 2001, 8, 1035-1055.
5. Güner, O. F. Pharmacophore perception, development, and use in drug design; International Univ Line, 2000; pp 128-159.
6. Wermuth, C. G. Pharmacophores: Historical Perspective and Viewpoint from a Medicinal Chemist. In Pharmacophores and Pharmacophore Searche; Langer, T.; Hoffmann, R. D., Ed.; Wiley: New York, 2006.
7. Yang, S.Y. Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today., 2010, 15, 444-450.
8. Barnum, D.; Greene, J.; Smellie, A.; Sprague, P. Identification of common functional configurations among molecules. J. Chem. Inf. Comput. Sci., 1996, 36, 563-571.
9. Li, H.; Sutter, J.; Hoffmann, R. HypoGen: an automated system for generating 3D predictive pharmacophore models. In Pharmacophore Perception, Development, and Use in Drug Design; Güner, O. F., International Univ Line, 2000; pp 171-188.
10. Dixon, S. L.; Smondyrev, A. M.; Knoll, E. H.; Rao, S. N.; Shaw, D. E.; Friesner, R. A. PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J. Comput. Aided Mol. Des., 2006, 20, 647-671.
11. Mannhold, R.; Kubinyi, H.; Folkers, G.; Langer, T.; Hoffmann, R. D. Pharmacophores and pharmacophore searches. Wiley-VCH, 2006,
12. Bultinck P. Computational medicinal chemistry for drug discovery; CRC Press, 2003.
13. Dror, O.; Shulman-Peleg, A.; Nussinov, R.; Wolfson, H. J. Predicting molecular interactions in silico: I. an updated guide to pharmacophore identification and its applications to drug design. Curr. Med. Chem., 2006, 3, 551-584.
14. Zhang, Q.; Muegge, I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring. J. Med. Chem., 2006, 49, 1536-1548.
15. Schmidtke, P.; Souaille, C.; Estienne, F.; Baurin, N.; Kroemer, R. T. Large-scale comparison of four binding site detection algorithms. J. Chem. Inf. Model., 2010, 50, 2191-2200.
16. Sanders, M. P. A.; McGuire, R.; Roumen, L.; de Esch, I. J. P.; de Vlieg, J.; Klomp, J. P. G.; de Graaf, C. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling. Med. Chem. Commun., 2012, 3, 28-38.
17. Wolber, G.; Langer, T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model., 2005, 45, 160-169.
18. Chen, J.; Lai, L. Pocket v. 2: further developments on receptorbased pharmacophore modeling. J. Chem. Inf. Model., 2006, 46, 2684-2691.
19. Ortuso, F.; Langer, T.; Alcaro, S. GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition. Bioinformatics., 2006, 22, 1449-1455.
20. Barillari, C.; Marcou, G.; Rognan, D. Hot-spots-guided receptorbased pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J. Chem. Inf. Model., 2008, 48, 1396-1410.
21. Sanders, M. P. A.; verhoeven, S.; Graaf, L. R.; Vroling, B.; Nabuurs, S. B.; de Vlieg, J.; Klomp, J. P. G. Snooker: a structurebased pharmacophore generation tool applied to class A GPCRs. J. Chem. Inf. Model., 2011, 51, 2277-92.
22. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today., 2006, 11, 580-594.
23. Schneider, G. Virtual screening: an endless staircase? Nat. Rev. Drug Discovery., 2010, 9, 273-276.
24. Pirhadi, S.; Ghasemi, J. B. Pharmacophore Identification, Molecular Docking, Virtual Screening, and In Silico ADME Studies of Non-Nucleoside Reverse Transcriptase Inhibitors. Mol. Info., 2012, 31, 856-866.
25. Muegge, I.; Oloff, S.; Advances in virtual screening. Drug Discov. Today. Technol., 2007, 3, 405-411.
26. Alvarez, J.; Shoichet, B. Virtual screening in drug discovery; CRC, 2005.
27. 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 Discovery., 2004, 3, 935-949.
28. Leach, A. R.; Gillet, V. J.; Lewis, R. A.; Taylor, R. Threedimensional pharmacophore methods in drug discovery. J. Med. Chem., 2010, 53, 539-558.
29. Goodford, P. J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem., 1985, 28, 849-857.
30. Cross, S.; Cruciani, G. Grid-derived structure-based 3D pharmacophores and their performance compared to docking. Drug Discov. Today. Technol., 2011, 7, 213-219.
31. Tintori, C.; Corradi, V.; Magnani, M.; Manetti, F.; Botta, M. Targets looking for drugs: A multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery. J. Chem. Inf. Model., 2008, 48, 2166-79.
32. Böhm, H. J. The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J. Comput-Aided. Mol. Des., 1992, 6, 61-78.
33. Allen, F. H. Kennard O. 3D search and research using the Cambridge Structural Database. Chem. Design. Autom. News., 1993, 8, 31-37.
34. Kirchhoff, P. D.; Brown, R.; Kahn, S.; Waldman, M.; Venkatachalam, C. M. Application of structure-based focusing to the estrogen receptor. J. Comput. Chem., 2001, 22, 993-1003.
35. Mason, J. S., Pickett, S. D. Partition-based selection. Perspect. Drug Discov. Des., 1997, 7, 85-114.
36. Pickett, S. D.; Mason, J. S.; McLay, I. M. Diversity profiling and design using 3D pharmacophores: Pharmacophore-derived queries (PDQ). J. Chem. Inf. Comput. Sci., 1996, 36, 1214-1223.
37. McGregor, M. J.; Muskal, S. M. Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J. Chem. Inf. Comput. Sci., 1999, 39, 569-574.
38. McGregor, M. J.; Muskal, S. M. Pharmacophore fingerprinting. 2. Application to primary library design. J. Chem. Inf. Comput. Sci., 2000, 40, 117-125.
39. Gohlke, H.; Hendlich, M.; Klebe, G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol., 2000, 295, 337-356.
40. Gohlke, H.; Hendlich, M.; Klebe, G. Predicting binding modes, binding affinities andhot spots' for protein-ligand complexes using a knowledge-based scoring function. Perspect. Drug Discov. Des., 2000, 20, 115-144.
41. Sotriffer, C. A.; Gohlke, H.; Klebe, G. Docking into knowledgebased potential fields: a comparative evaluation of DrugScore. J. Med. Chem., 2002, 45, 1967-1970.
42. Miranker, A.; Karplus, M.; Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins: Struct. Funct. Bioinf., 2004, 11, 29-34.
43. Jorgensen, W. L. BOSS Version 4.1. New Haven, CT. Yale University 1999.
44. Meagher, K. L.; Carlson, H. A. Incorporating protein flexibility in structure-based drug discovery: using HIV-1 protease as a test case. J. Am. Chem. Soc., 2004, 126, 13276-13281.
45. Meagher, K. L.; Lerner, M. G.; Carlson, H. A. Refining the multiple protein structure pharmacophore method: consistency across three independent HIV-1 protease models. J. Med. Chem., 2006, 49, 3478-3484.
46. Bowman, A. L.; Nikolovska-Coleska, Z.; Zhong, H.; Wang, S.; Carlson, H. A. Small molecule inhibitors of the MDM2-p53 interaction discovered by ensemble-based receptor models. J. Am. Chem. Soc., 2007, 129, 12809-12814.
47. Lexa, K. W.; Carlson, H. A. Full protein flexibility is essential for proper hot-spot mapping. J. Am. Chem. Soc., 2010, 133, 200-202.
48. Deng, J.; Lee, K. W.; Sanchez, T.; Cui, M.; Neamati, N.; Briggs, J. M. Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors. J. Med. Chem., 2005, 48, 1496-1505.
49. Deng, J.; Sanchez, T.; Neamati, N.; Briggs, J. M. Dynamic pharmacophore model optimization: identification of novel HIV-1 integrase inhibitors. J. Med. Chem., 2006, 49, 1684-1692.
50. Wang, R.; Gao, Y.; Lai, L. LigBuilder: a multi-purpose program for structure-based drug design. J. Mol. Model., 2000, 6, 498-516.
51. Steindl, T. M.; Schuster, D.; Laggner, C.; Langer, T. Parallel screening: a novel concept in pharmacophore modeling and virtual screening. J. Chem. Inf. Model., 2006, 46, 2146-2157.
52. Pandit, D.; So, S. S.; Sun, H. Enhancing specificity and sensitivity of pharmacophore-based virtual screening by incorporating chemical and shape features-a case study of HIV protease inhibitors. J. Chem. Inf. Model., 2006, 46, 1236-1244.
53. Toba, S.; Srinivasan, J.; Maynard, A. J.; Sutter, J. Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR. J. Chem. Inf. Model., 2006, 46, 728-735.
54. Greenidge, P. A.; Carlsson, B.; Bladh, L. G.; Gillner, M. Pharmacophores incorporating numerous excluded volumes defined by Xray crystallographic structure in three-dimensional database searching: application to the thyroid hormone receptor. J. Med. Chem., 1998, 41, 2503-2512.
55. Dong, X.; Ebalunode, J. O.; Yang, S. Y.; Zheng, W. Receptor- Based Pharmacophore and Pharmacophore Key Descriptors for Virtual Screening and QSAR Modeling. Curr. Comput-Aided. Drug Des., 2011, 7, 181-189.
56. Gao, Q.; Yang, L.; Zhu, Y. Pharmacophore based drug design approach as a practical process in drug discovery. Curr. Comput- Aided. Drug Des., 2010, 6, 37-49.
57. Baroni, M.; Cruciani, G.; Sciabola, S.; Perruccio, F.; Mason, J. S. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for Ligands and Proteins (FLAP): theory and application. J. Chem. Inf. Model., 2007, 47, 279-294.
58. Ebalunode, J. O.; Ouyang, Z.; Liang, J.; Zheng, W. Novel approach to structure-based pharmacophore search using computational geometry and shape matching techniques. J. Chem. Inf. Model., 2008, 48, 889-901.
59. Miteva, M. A.; Lee, W. H.; Montes, M. O.; Villoutreix, B. O. Fast structure-based virtual ligand screening combining FRED, DOCK, and Surflex. J. Med. Chem., 2005, 48, 6012-6022.
60. Ebalunode, J. O.; Dong, X.; Ouyang, Z.; Liang, J.; Eckenhoff, R. G.; Zheng, W. Structure-based shape pharmacophore modeling for the discovery of novel anesthetic compounds. Bioorg. Med. Chem., 2009, 17, 5133-5138.
61. Salam, N. K.; Nuti, R.; Sherman, W. Novel method for generating structure-based pharmacophores using energetic analysis. J. Chem. Inf. Model., 2009, 49, 2356-2368.
62. Friesner, R. A.; Murphy, R. B.; Repasky, M. P.; Frye, L. L.; Greenwood, J. R.; Halgren, T. A.; Sanschagrin, P. C.; Mainz, D. T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J. Med. Chem., 2006, 49, 6177-6196.
63. Zou, J.; Xie, H. Z.; Yang, S. Y.; Chen, J. J.; Ren, J. X.; Wei, Y. Q. Towards more accurate pharmacophore modeling: Multicomplexbased comprehensive pharmacophore map and most-frequentfeature pharmacophore model of CDK2. J. Mol. Graph. Model., 2008, 27, 430-438.
64. Huang, Q.; Li, L. L.; Yang, S. Y. PhDD: A new pharmacophorebased de novo design method of drug-like molecules combined with assessment of synthetic accessibility. J. Mol. Graph. Model., 2010, 28, 775-787.
65. Zhao, H. Scaffold selection and scaffold hopping in lead generation: a medicinal chemistry perspective. Drug Discov. Today., 2007, 12, 149-155.
66. Dror, O.; Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H. J. Novel approach for efficient pharmacophore-based virtual screening: method and applications. J. Chem. Inf. Model., 2009, 49, 2333-2343.
67. Hendlich, M.; Bergner, A.; Gunther, J.; Klebe, G. Relibase: design and development of a database for comprehensive analysis of protein- ligand interactions. J. Mol. Biol., 2003, 326, 607-620.
68. Günther, J.; Bergner, A.; Hendlich, M.; Klebe, G. Utilising structural knowledge in drug design strategies: applications using Relibase. J. Mol. Biol., 2003, 326, 621-636.
69. Bergner, A.; Günther, J.; Hendlich, M.; Klebe, G.; Verdonk, M. Use of Relibase for retrieving complex three-dimensional interaction patterns including crystallographic packing effects. Biopolymers., 2001, 61, 99-110.
70. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 1997, 23, 3-25.
71. Wang, B.; Ekins, S. Computer applications in pharmaceutical research and development; Wiley-Interscience., 2006.
72. Schneider, G.; Schneider, P.; Renner, S. Scaffold-Hopping: How Far Can You Jump? QSAR. Comb. Sci., 2006, 25, 1162-1171.
73. H. Pharmacophore-based virtual screening. Curr. Med. Chem., 2008, 15, 1018-1024.
74. Green, D. V. S. Virtual screening of chemical libraries for drug discovery. Expert Opin. Drug Discovery., 2008, 3, 1011-1026.
75. Clark, D. E. What has virtual screening ever done for drug discovery? Expert Opin. Drug Discovery., 2008, 3, 841-851.
76. Von Itzstein, M.; Wu, W. Y.; Kok, G. B.; Pegg, M. S.; Dyason, J. C.; Jin, B.; Van Phan, T.; Smythe, M. L.; White, H. F.; Oliver, S. W. Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature., 1993, 363, 418-423.
77. Güner, O. F. History and evolution of the pharmacophore concept in computer-aided drug design. Curr. Top. Med. Chem., 2002, 2, 1321-1332.
78. Yingling, J. M.; Blanchard, K. L.; Sawyer, J. S. Development of TGF-_ signalling inhibitors for cancer therapy. Nat. Rev. Drug Discov., 2004, 3, 1011-1022.
79. Vijayan, R. K.; Prabu, M.; Mascarenhas, N. M.; Ghoshal, N. Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors. J. Chem. Inf. Model., 2009, 49, 647-657.
80. Evers, A.; Klebe, G. Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligandsupported homology model. J. Med. Chem., 2004, 47, 5381-5392.
81. Pirard, B.; Brendel, J.; Peukert, S. The discovery of Kv1. 5 blockers as a case study for the application of virtual screening approaches. J. Chem. Inf. Model., 2005, 45, 477-485.
82. Löwer, M.; Proschak, E. Structure-Based Pharmacophores for Virtual Screening. Mol. Info., 2011, 30, 398-404.
83. Steindl, T. M.; Schuster, D.; Laggner, C.; Chuang, K.; Hoffmann, R. D.; Langer, T. Parallel screening and activity profiling with HIV protease inhibitor pharmacophore models. J. Chem. Inf. Model., 2007, 47, 563-571.
84. Kubinyi, H. In search for new leads. In EFMC - Yearbook; LDOrganisation: Louvain-la-Neuve; Belgium, 2003; pp 14-28.
85. Böhm, H. J.; Klebe, G.; Kubinyi, H. Wirkstoffdesign-Der Weg zum Arzneimittel Spektrum; Akademischer Verlag: Germany, 1996; pp 451-466.
86. Schuster, D.; Langer, T. The identification of ligand features essential for PXR activation by pharmacophore modeling. J. Chem. Inf. Model., 2005, 45, 431-439.
87. Sanguinetti, M. C.; Mitcheson, J. S. Predicting drug-hERG channel interactions that cause acquired long QT syndrome. Trends. Pharmacol. Sci., 2005, 26, 119-124.
88. Tulp, M.; Bohlin, L. Rediscovery of known natural compounds: nuisance or goldmine?. Bioorg. Med. Chem., 2005, 13, 5274-5282.
89. Schuster, D.; Waltenberger, B.; Kirchmair, J.; Distinto, S.; Markt, P.; Stuppner, H. Rollinger, J. M.; Wolber, G. Predicting Cyclooxygenase Inhibition by Three-Dimensional Pharmacophoric Profiling. Part I: Model Generation, Validation and Applicability in Ethnopharmacology. Mol. Info., 2010, 29, 75-86.
90. Rollinger, J. M. Accessing target information by virtual parallel screening-the impact on natural product research. Phytochem. Lett., 2009, 2, 53-58.
91. Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5, 993-996.
92. Szuromi, P.; Vinson, V.; Marshall, E. Rethinking drug discovery. Sci. Signal., 2004, 303, 1795-1729.
93. Mattson, M. P. Pathways towards and away from Alzheimer's disease. Nature., 2004, 430, 631-639.
94. Wei, D.; Jiang, X.; Zhou, L.; Chen, J.; Chen, Z.; He, C.; Yang, K.; Liu, Y.; Pei, J.; Lai, L. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J. Med. Chem., 2008, 51, 7882-7888.
95. Smalley, K. S. M.; Haass, N. K.; Brafford, P. A.; Lioni, M.; Flaherty, K. T.; Herlyn, M. Multiple signaling pathways must be targeted to overcome drug resistance in cell lines derived from melanoma metastases. Mol. Cancer. Ther., 2006, 5, 1136-1144.
96. Keith, C. T.; Borisy, A. A.; Stockwell, B. R. Multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov., 2005, 4, 71-78.
97. Larder, B. A.; Kemp, S. D.; Harrigan, P. R. Potential mechanism for sustained antiretroviral efficacy of AZT-3TC combination therapy. Science., 1995, 269, 696-699.
98. Zhang, X.; Crespo, A.; Fernández, A. Turning promiscuous kinase inhibitors into safer drugs. Trends. Biotechnol., 2008, 26, 295-301.
99. Shoichet, B. K.; Virtual screening of chemical libraries. Nature., 2004, 432, 862-865.
100. Ma, X. H.; Wang, R.; Yang, S. Y.; Li, Z. R.; Xue, Y.; Wei, Y. C.; Low, B. C.; Chen, Y. Z. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J. Chem. Inf. Model., 2008, 48, 1227-1237.
101. Ma, X. H.; Shi, Z.; Tan, C.; Jiang, Y.; Go, M. L.; Low, B. C.; Chen, Y. Z. In-Silico Approaches to Multi-target Drug Discovery. Pharm. Res., 2010, 27, 739-749.
102. Morphy, R.; Rankovic, Z. The physicochemical challenges of designing multiple ligands. J. Med. Chem., 2006, 49, 4961-4970.
103. Clemente, J. C.; Govindasamy, L.; Madabushi, A.; Fisher S. Z.; Moose, R.E.; Yowell, C. A.; Hidaka, K.; Kimura, T.; Hayashi, Y.; Kiso, Y. Structure of the aspartic protease plasmepsin 4 from the malarial parasite Plasmodium malariae bound to an allophenylnorstatine- based inhibitor. Acta. Crystallogr., Sect. D: Biol. Crystallogr., 2006, 62, 246-252.
104. Langer, T. Pharmacophores in drug research. Mol. Info., 2010, 29, 470-475.
105. van Westen, G. J. P.; Wegner, J. K.; Ijzerman, A. P.; van Vlijmen, H. W. T.; Bender, A. Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. Med. Chem. Commun., 2011, 2, 16-30.
106. Güner, O. F. The impact of pharmacophore modeling in drug design. IDrugs., 2005, 8, 567-572.
107. Pirhadi, S.; Ghasemi, J. B. 3D-QSAR analysis of human immunodeficiency virus entry-1 inhibitors by CoMFA and CoMSIA. Eur. J. Med. Chem., 2010, 45, 4897-4903.
108. Ghasemi, J. B.; Shiri, F. Molecular docking and 3D-QSAR studies of falcipain inhibitors using CoMFA, CoMSIA, and Open3DQSAR. Med. Chem. Res., 2012, 21, 1-19.
109. Ghasemi, J. B.; Safavi-Sohi R.; Barbosa, E. G. 4D-LQTA-QSAR and docking study on potent gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling. Mol. Diversity., 2012, 16, 203-213.
110. Dong, X.; Ebalunode, J. O.; Cho, S. J.; Zheng, W. A Novel Structure- Based Multimode QSAR Method Affords Predictive Models for Phosphodiesterase Inhibitors. J. Chem. Inf. Model., 2010, 50, 240-250.
111. Dong, X.; Zheng, W. A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. Curr. Chem. Genomics., 2008, 2, 29-39.
112. Kar, S.; Roy, K. QSAR of phytochemicals for the design of better drugs. Expert Opin. Drug Discovery., 2012, 7, 877-902.
113. Mitra, I.; Saha, A.; Roy, K. In silico development, validation and comparison of predictive QSAR models for lipid peroxidation inhibitory activity of cinnamic acid and caffeic acid derivatives using multiple chemometric and cheminformatics tools. J. Mol. Model., 2012, 18 3951-3967.