A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands

PLoS ONE

Volumn 7, Issue 6, 2012, Pages

  Zhang, Jingxian ^a,b   Han, Bucong ^b   Wei, Xiaona ^b   Tan, Chunyan ^a   Chen, Yuzong ^a,b   Jiang, Yuyang ^a  

^a TSINGHUA UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

DOPAMINE 1 RECEPTOR; DOPAMINE 2 RECEPTOR; DOPAMINE 3 RECEPTOR; DOPAMINE 4 RECEPTOR; DOPAMINE RECEPTOR; ESTROGEN RECEPTOR;

ARTICLE; BINDING AFFINITY; CONTROLLED STUDY; INTERMETHOD COMPARISON; LIGAND BINDING; MATHEMATICAL MODEL; MOLECULAR LIBRARY; MOLECULAR MODEL; RECEPTOR BINDING; SUPPORT VECTOR MACHINE;

LIGANDS; PROTEIN BINDING; RECEPTORS, DOPAMINE; SUPPORT VECTOR MACHINES;

EID: 84862490169     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0039076     Document Type: Article

References (74)

1. Zeng C, Jose PA, (2011) Dopamine Receptors: Important Antihypertensive Counterbalance against Hypertensive Factors. Hypertension 57: 11-17.
2. Cho DI, Zheng M, Kim KM, (2010) Current Perspectives on the Selective Regulation of Dopamine D2 and D3 Receptors. Arch Pharm Res 33: 1521-1538.
3. Zhang A, Neumeyer JL, Baldessarini RJ, (2007) Recent Progress in Development of Dopamine Receptor Subtype-Selective Agents: Potential Therapeutics for Neurological and Psychiatric Disorders. Chem Rev 107: 274-302.
4. Lober S, Hubner H, Tschammer N, Gmeiner P, (2011) Recent Advances in the Search for D3- and D4-Selective Drugs: Probes, Models and Candidates. Trends Pharmacol Sci 32: 148-157.
5. Micheli F, (2011) Recent Advances in the Development of Dopamine D3 Receptor Antagonists: A Medicinal Chemistry Perspective. ChemMedChem 6: 1152-1162.
6. Heidbreder CA, Newman AH, (2010) Current Perspectives on Selective Dopamine D(3) Receptor Antagonists as Pharmacotherapeutics for Addictions and Related Disorders. Ann N Y Acad Sci 1187: 4-34.
7. Chien EY, Liu W, Zhao Q, Katritch V, Han GW, et al. (2010) Structure of the Human Dopamine D3 Receptor in Complex with a D2/D3 Selective Antagonist. Science 330: 1091-1095.
8. Sibley DR, Monsma FJ Jr, (1992) Molecular Biology of Dopamine Receptors. Trends Pharmacol Sci 13: 61-69.
9. Simpson MM, Ballesteros JA, Chiappa V, Chen J, Suehiro M, et al. (1999) Dopamine D4/D2 Receptor Selectivity Is Determined by a Divergent Aromatic Microdomain Contained within the Second, Third, and Seventh Membrane-Spanning Segments. Mol Pharmacol 56: 1116-1126.
10. Wang Q, Mach RH, Luedtke RR, Reichert DE, (2010) Subtype Selectivity of Dopamine Receptor Ligands: Insights from Structure and Ligand-Based Methods. J Chem Inf Model 50: 1970-1985.
11. Lopez L, Selent J, Ortega R, Masaguer CF, Dominguez E, et al. (2010) Synthesis, 3d-Qsar, and Structural Modeling of Benzolactam Derivatives with Binding Affinity for the D(2) and D(3) Receptors. ChemMedChem 5: 1300-1317.
12. Cha MY, Lee IY, Cha JH, Choi KI, Cho YS, et al. (2003) Qsar Studies on Piperazinylalkylisoxazole Analogues Selectively Acting on Dopamine D3 Receptor by Hqsar and Comfa. Bioorg Med Chem 11: 1293-1298.
13. Audouze K, Nielsen EO, Peters D, (2004) New Series of Morpholine and 1,4-Oxazepane Derivatives as Dopamine D4 Receptor Ligands: Synthesis and 3d-Qsar Model. J Med Chem 47: 3089-3104.
14. Clark RD, Abrahamian E, (2009) Using a Staged Multi-Objective Optimization Approach to Find Selective Pharmacophore Models. J Comput Aided Mol Des 23: 765-771.
15. Salama I, Hocke C, Utz W, Prante O, Boeckler F, et al. (2007) Structure-Selectivity Investigations of D2-Like Receptor Ligands by Comfa and Comsia Guiding the Discovery of D3 Selective Pet Radioligands. J Med Chem 50: 489-500.
16. Carro L, Ravina E, Dominguez E, Brea J, Loza MI, et al. (2009) Synthesis and Binding Affinity of Potential Atypical Antipsychotics with the Tetrahydroquinazolinone Motif. Bioorg Med Chem Lett 19: 6059-6062.
17. Huber D, Hubner H, Gmeiner P, (2009) 1,1′-Disubstituted Ferrocenes as Molecular Hinges in Mono- and Bivalent Dopamine Receptor Ligands. J Med Chem 52: 6860-6870.
18. Ma XH, Wang R, Tan CY, Jiang YY, Lu T, et al. (2010) Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines. Mol Pharm 7: 1545-1560.
19. Han LY, Ma XH, Lin HH, Jia J, Zhu F, et al. (2008) A Support Vector Machines Approach for Virtual Screening of Active Compounds of Single and Multiple Mechanisms from Large Libraries at an Improved Hit-Rate and Enrichment Factor. J Mol Graph Model 26: 1276-1286.
20. Li H, Yap CW, Ung CY, Xue Y, Li ZR, et al. (2007) Machine Learning Approaches for Predicting Compounds That Interact with Therapeutic and Admet Related Proteins. J Pharm Sci 96: 2838-2860.
21. Mahe P, Vert JP, (2009) Virtual Screening with Support Vector Machines and Structure Kernels. Comb Chem High Throughput Screen 12: 409-423.
22. Monge A, Arrault A, Marot C, Morin-Allory L, (2006) Managing, Profiling and Analyzing a Library of 2.6 Million Compounds Gathered from 32 Chemical Providers. Molecular diversity 10: 389-403.
23. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, et al. (2009) Pubchem: A Public Information System for Analyzing Bioactivities of Small Molecules. Nucleic Acids Res 37: W623-633.
24. Irwin JJ, Shoichet BK, (2005) Zinc-a Free Database of Commercially Available Compounds for Virtual Screening. Journal of chemical information and modeling 45: 177-182.
25. Bellis LJ, Akhtar R, Al-Lazikani B, Atkinson F, Bento AP, et al. (2011) Collation and Data-Mining of Literature Bioactivity Data for Drug Discovery. Biochemical Society transactions 39: 1365-1370.
26. Wester MJ, Pollock SN, Coutsias EA, Allu TK, Muresan S, et al. (2008) Scaffold Topologies. 2. Analysis of Chemical Databases. J Chem Inf Model 48: 1311-1324.
27. Verheij HJ, (2006) Leadlikeness and Structural Diversity of Synthetic Screening Libraries. Molecular diversity 10: 377-388.
28. Michielan L, Terfloth L, Gasteiger J, Moro S, (2009) Comparison of Multilabel and Single-Label Classification Applied to the Prediction of the Isoform Specificity of Cytochrome P450 Substrates. J Chem Inf Model 49: 2588-2605.
29. Mishra NK, Agarwal S, Raghava GP, (2010) Prediction of Cytochrome P450 Isoform Responsible for Metabolizing a Drug Molecule. BMC Pharmacol 10: 8.
30. Tsoumakas G, Katakis I, Vlahavas I, (2010) Mining Multi-Label Data. In: Maimon O, Rokach L, editors. pp. 667-685 editors. Data Mining and Knowledge Discovery Handbook: Springer US.
31. Zhang M-L, Zhou Z-H, (2007) Ml-Knn: A Lazy Learning Approach to Multi-Label Learning. Pattern Recognition 40: 2038-2048.
32. Tsoumakas G, Katakis I, (2007) Multi-Label Classification: An Overview. Int J Data Warehousing and Mining 2007: 1-13.
33. Schietgat L, Vens C, Struyf J, Blockeel H, Kocev D, et al. (2010) Predicting Gene Function Using Hierarchical Multi-Label Decision Tree Ensembles. BMC Bioinformatics 11: 2.
34. Jenner P, (2003) Dopamine Agonists, Receptor Selectivity and Dyskinesia Induction in Parkinson's Disease. Curr Opin Neurol 16: S3-7.
35. McCall RB, Lookingland KJ, Bedard PJ, Huff RM, (2005) Sumanirole, a Highly Dopamine D2-Selective Receptor Agonist: In Vitro and in Vivo Pharmacological Characterization and Efficacy in Animal Models of Parkinson's Disease. J Pharmacol Exp Ther 314: 1248-1256.
36. Singer C, Lamb J, Ellis A, Layton G, (2007) A Comparison of Sumanirole Versus Placebo or Ropinirole for the Treatment of Patients with Early Parkinson's Disease. Mov Disord 22: 476-482.
37. Pilla M, Perachon S, Sautel F, Garrido F, Mann A, et al. (1999) Selective Inhibition of Cocaine-Seeking Behaviour by a Partial Dopamine D3 Receptor Agonist. Nature 400: 371-375.
38. Boeckler F, Gmeiner P, (2006) The Structural Evolution of Dopamine D3 Receptor Ligands: Structure-Activity Relationships and Selected Neuropharmacological Aspects. Pharmacol Ther 112: 281-333.
39. Albersen M, Shindel AW, Mwamukonda KB, Lue TF, (2010) The Future Is Today: Emerging Drugs for the Treatment of Erectile Dysfunction. Expert Opin Emerg Drugs 15: 467-480.
40. Lober S, Tschammer N, Hubner H, Melis MR, Argiolas A, et al. (2009) The Azulene Framework as a Novel Arene Bioisostere: Design of Potent Dopamine D4 Receptor Ligands Inducing Penile Erection. ChemMedChem 4: 325-328.
41. Zhang J, Xiong B, Zhen X, Zhang A, (2009) Dopamine D1 Receptor Ligands: Where Are We Now and Where Are We Going. Med Res Rev 29: 272-294.
42. Aloisi G, Silvano E, Rossi M, Millan MJ, Maggio R, (2011) Differential Induction of Adenylyl Cyclase Supersensitivity by Antiparkinson Drugs Acting as Agonists at Dopamine D1/D2/D3 Receptors Vs D2/D3 Receptors Only: Parallel Observations from Co-Transfected Human and Native Cerebral Receptors. Neuropharmacology 60: 439-445.
43. Overington J, (2009) Chembl. An Interview with John Overington, Team Leader, Chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (Embl-Ebi). Interview by Wendy A. Warr. J Comput Aided Mol Des 23: 195-198.
44. Herm L, Berenyi S, Vonk A, Rinken A, Sipos A, (2009) N-Substituted-2-Alkyl- and 2-Arylnorapomorphines: Novel, Highly Active D2 Agonists. Bioorg Med Chem 17: 4756-4762.
45. Enguehard-Gueiffier C, Gueiffier A, (2006) Recent Progress in Medicinal Chemistry of D4 Agonists. Curr Med Chem 13: 2981-2993.
46. Ehrlich K, Gotz A, Bollinger S, Tschammer N, Bettinetti L, et al. (2009) Dopamine D2, D3, and D4 Selective Phenylpiperazines as Molecular Probes to Explore the Origins of Subtype Specific Receptor Binding. J Med Chem 52: 4923-4935.
47. Li ZR, Han LY, Xue Y, Yap CW, Li H, et al. (2007) Model-Molecular Descriptor Lab: A Web-Based Server for Computing Structural and Physicochemical Features of Compounds. Biotechnol Bioeng 97: 389-396.
48. Shi Z, Ma XH, Qin C, Jia J, Jiang YY, et al. (2012) Combinatorial Support Vector Machines Approach for Virtual Screening of Selective Multi-Target Serotonin Reuptake Inhibitors from Large Compound Libraries. J Mol Graph Model 32: 49-66.
49. Vapnik VN, (1995) The Nature of Statistical Learning Theory. New York: Springer.
50. Trotter MWB, Buxton BF, Holden SB, (2001) Support Vector Machines in Combinatorial Chemistry. Meas Control 34: 235-239.
51. Burbidge R, Trotter M, Buxton B, Holden S, (2001) Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis. Comput Chem 26: 5-14.
52. Czerminski R, Yasri A, Hartsough D, (2001) Use of Support Vector Machine in Pattern Classification: Application to Qsar Studies. Quantitative Structure-Activity Relationships 20: 227-240.
53. Johnson RA, Wichern DW, (1982) Applied Multivariate Statistical Analysis. Englewood Cliffs, NJ: Prentice Hall.
54. Quinlan JR, (1993) C4.5: Programs for Machine Learning. San Mateo, Calif: Morgan Kaufmann.
55. Matthews BW, (1975) Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme. Biochimica et Biophysica Acta 405: 442-451.
56. Willett P, (1998) Chemical Similarity Searching. J Chem Inf Comput Sci 38: 983-996.
57. Yap CW, (2011) Padel-Descriptor: An Open Source Software to Calculate Molecular Descriptors and Fingerprints. J Comput Chem 32: 1466-1474.
58. Huang N, Shoichet BK, Irwin JJ, (2006) Benchmarking Sets for Molecular Docking. J Med Chem 49: 6789-6801.
59. Bostrom J, Hogner A, Schmitt S, (2006) Do Structurally Similar Ligands Bind in a Similar Fashion? J Med Chem 49: 6716-6725.
60. Xue Y, Li ZR, Yap CW, Sun LZ, Chen X, et al. (2004) Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents. J Chem Inf Comput Sci 44: 1630-1638.
61. Kohavi R, John GH, (1997) Wrappers for Feature Subset Selection. Artificial Intelligence 97: 273-324.
62. Durrant JD, McCammon JA, (2010) Computer-Aided Drug-Discovery Techniques That Account for Receptor Flexibility. Curr Opin Pharmacol 10: 770-774.
63. Sun H, (2008) Pharmacophore-Based Virtual Screening. Curr Med Chem 15: 1018-1024.
64. Sprous DG, Palmer RK, Swanson JT, Lawless M, (2010) Qsar in the Pharmaceutical Research Setting: Qsar Models for Broad, Large Problems. Curr Top Med Chem 10: 619-637.
65. Willett P, (2011) Similarity Searching Using 2d Structural Fingerprints. Methods Mol Biol 672: 133-158.
66. Ma XH, Jia J, Zhu F, Xue Y, Li ZR, et al. (2009) Comparative Analysis of Machine Learning Methods in Ligand-Based Virtual Screening of Large Compound Libraries. Comb Chem High Throughput Screen 12: 344-357.
67. Sato T, Honma T, Yokoyama S, (2010) Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening. J Chem Inf Model 50: 170-185.
68. Talevi A, Gavernet L, Bruno-Blanch LE, (2009) Combined Virtual Screening Strategies. Current Computer - Aided Drug Design 5: 23-37.
69. Bender A, Jenkins JL, Glick M, Deng Z, Nettles JH, et al. (2006) "Bayes Affinity Fingerprints" Improve Retrieval Rates in Virtual Screening and Define Orthogonal Bioactivity Space: When Are Multitarget Drugs a Feasible Concept? J Chem Inf Model 46: 2445-2456.
70. Givehchi A, Bender A, Glen RC, (2006) Analysis of Activity Space by Fragment Fingerprints, 2d Descriptors, and Multitarget Dependent Transformation of 2d Descriptors. J Chem Inf Model 46: 1078-1083.
71. Renner S, Derksen S, Radestock S, Morchen F, (2008) Maximum Common Binding Modes (Mcbm): Consensus Docking Scoring Using Multiple Ligand Information and Interaction Fingerprints. J Chem Inf Model 48: 319-332.
72. Erhan D, L'Heureux P J, Yue SY, Bengio Y, (2006) Collaborative Filtering on a Family of Biological Targets. J Chem Inf Model 46: 626-635.
73. Dragos H, Gilles M, Alexandre V, (2009) Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of Qsar Models. J Chem Inf Model 49: 1762-1776.
74. Liu XH, Ma XH, Tan CY, Jiang YY, Go ML, et al. (2009) Virtual Screening of Abl Inhibitors from Large Compound Libraries by Support Vector Machines. J Chem Inf Model 49: 2101-2110.