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Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

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Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1260))

Abstract

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

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References

  1. Myint KZ, Xie X-Q (2010) Recent advances in fragment-based QSAR and multi-dimensional QSAR methods. Int J Mol Sci 11(10):3846–3866

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Perkins R, Fang H, Tong W et al (2003) Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ Toxicol Chem 22(8):1666–1679

    Article  CAS  PubMed  Google Scholar 

  3. Salum L, Andricopulo A (2009) Fragment-based QSAR: perspectives in drug design. Mol Divers 13(3):277

    Article  CAS  PubMed  Google Scholar 

  4. Chen JZ, Wang J, Xie XQ (2007) GPCR structure-based virtual screening approach for CB2 antagonist search. J Chem Inf Model 47(4):1626–1637. doi:10.1021/ci7000814

    Article  CAS  PubMed  Google Scholar 

  5. Wang L, Ma C, Wipf P et al (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15:395–406, PMID: 23292636

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Tandon M, Wang L, Xu Q et al (2012) A targeted library screen reveals a new inhibitor scaffold for protein kinase D. PLoS One 7(9):e44653. doi:10.1371/journal.pone.0044653, PMID: 23028574

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Ma C, Wang L, Yang P et al (2013) LiCABEDS II. Modeling of ligand selectivity for G-protein coupled cannabinoid receptors. J Chem Inf Model 53(1):11–26. doi:10.1021/ci3003914

    Article  PubMed Central  PubMed  Google Scholar 

  8. Wang L, Ma C, Wipf P et al (2012) Linear and nonlinear support vector machine for the classification of human 5-HT1A ligand functionality. Mol Inf 31(1):85–95. doi:10.1002/minf.201100126

    Article  Google Scholar 

  9. Myint K, Xie X-Q (2011) Fragment-based QSAR algorithm development for compound bioactivity prediction. SAR QSAR Environ Res 22(3):385–410

    Article  CAS  PubMed  Google Scholar 

  10. Chen JZ, Myint KZ, Xie X-Q (2011) New QSAR prediction models derived from GPCR CB2-antagonistic triaryl bis-sulfone analogues by a combined molecular morphological and pharmacophoric approach. SAR QSAR Environ Res 22(5–6):525–544. doi:10.1080/1062936x.2011.569948

    Article  CAS  PubMed  Google Scholar 

  11. Chen J-Z, Han X-W, Liu Q et al (2006) 3D-QSAR studies of arylpyrazole antagonists of cannabinoid receptor subtypes CB1 and CB2. A combined NMR and CoMFA approach. J Med Chem 49(2):625–636

    Article  CAS  PubMed  Google Scholar 

  12. Vilar S, Santana L, Uriarte E (2006) Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action. J Med Chem 49(3):1118–1124. doi:10.1021/jm050932j

    Article  CAS  PubMed  Google Scholar 

  13. González-Díaz H, Bonet I, Terán C et al (2007) ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur J Med Chem 42(5):580–585. doi:10.1016/j.ejmech.2006.11.016

    Article  PubMed  Google Scholar 

  14. Patra JC, Chua BH (2011) Artificial neural network-based drug design for diabetes mellitus using flavonoids. J Comput Chem 32(4):555–567. doi:10.1002/jcc.21641

    Article  CAS  PubMed  Google Scholar 

  15. Dimitrov I, Naneva L, Bangov I et al (2014) Allergenicity prediction by artificial neural networks. J Chemometrics. doi:10.1002/cem.2597

    Google Scholar 

  16. Vanyúr R, Héberger K, Kövesdi I et al (2002) Prediction of tumoricidal activity and accumulation of photosensitizers in photodynamic therapy using multiple linear regression and artificial neural networks. Photochem Photobiol 75(5):471–478. doi:10.1562/0031-8655(2002)0750471potaaa2.0.co2

    Article  Google Scholar 

  17. Myint K-Z, Wang L, Tong Q et al (2012) Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 9(10):2912–2923. doi:10.1021/mp300237z

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Molnár L, Keserű GM (2002) A neural network based virtual screening of cytochrome P450 3A4 inhibitors. Bioorg Med Chem Lett 12(3):419–421. doi:10.1016/s0960-894x(01)00771-5

    Article  PubMed  Google Scholar 

  19. Muresan S, Sadowski J (2005) “In-House Likeness”: comparison of large compound collections using artificial neural networks. J Chem Inf Model 45(4):888–893. doi:10.1021/ci049702o

    Article  CAS  PubMed  Google Scholar 

  20. Wang L, Xie XQ (2012) Cannabinoid Ligand Database. Accessed Nov 2011

    Google Scholar 

  21. Sutherland JJ, O’Brien LA, Weaver DF (2004) A comparison of methods for modeling quantitative structure–activity relationships. J Med Chem 47(22):5541–5554. doi:10.1021/jm0497141

    Article  CAS  PubMed  Google Scholar 

  22. Greenidge PA, Carlsson B, Bladh L-G et al (1998) Pharmacophores incorporating numerous excluded volumes defined by x-ray crystallographic structure in three-dimensional database searching: application to the thyroid hormone receptor. J Med Chem 41(14):2503–2512

    Article  CAS  PubMed  Google Scholar 

  23. Mathworks (2007) MATLAB. 7.5.0.342 (R2007b) edn, Natick, MA

    Google Scholar 

  24. O’Boyle N, Banck M, James C et al (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33

    Article  PubMed Central  PubMed  Google Scholar 

  25. Durant JL, Leland BA, Henry DR et al (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273–1280. doi:10.1021/ci010132r

    Article  CAS  PubMed  Google Scholar 

  26. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754. doi:10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  27. Cramer R, Patterson D, Bunce J (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  CAS  PubMed  Google Scholar 

  28. Klebe G (1998) Comparative molecular similarity indices analysis: CoMSIA. 3D QSAR in Drug Design. Three-Dimensional Quantitative Structure Activity Relationships 3:87–104

    Google Scholar 

  29. Lowis D (1997) HQSAR: a new, highly predictive QSAR technique. Tripos Technical Notes 1(5):17

    Google Scholar 

  30. Jain AN (2004) Ligand-based structural hypotheses for virtual screening. J Med Chem 47(4):947–961

    Article  CAS  PubMed  Google Scholar 

  31. Ferguson AM, Heritage T, Jonathon P et al (1997) EVA: a new theoretically based molecular descriptor for use in QSAR/QSPR analysis. J Comput Aided Mol Des 11(2):143–152. doi:10.1023/a:1008026308790

    Article  CAS  PubMed  Google Scholar 

  32. Agrafiotis DK, Cedeño W, Lobanov VS (2002) On the use of neural network ensembles in QSAR and QSPR. J Chem Inf Comput Sci 42(4):903–911. doi:10.1021/ci0203702

    Article  CAS  PubMed  Google Scholar 

  33. Bender A, Jenkins JL, Scheiber J et al (2009) How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model 49(1):108–119. doi:10.1021/ci800249s

    Article  CAS  PubMed  Google Scholar 

  34. Glem R, Bender A, Arnby C et al (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9(3):199–204

    PubMed  Google Scholar 

  35. Bellis LJ, Akhtar R, Al-Lazikani B et al (2011) Collation and data-mining of literature bioactivity data for drug discovery. Biochem Soc Trans 39(5):1365–1370. doi:10.1042/BST0391365, BST0391365 [pii]

    Article  CAS  PubMed  Google Scholar 

  36. Collins J, Crowell J (2011) Drug Synthesis and Chemistry Branch, Developmental Therapeutics Program (DTP), Division of Cancer Treatment and Diagnosis, National Cancer Institute. http://dtp.nci.nih.gov/

  37. Tripos (2012) SYBYL-X 1.2. 1699 South Hanley Rd., St. Louis, Missouri, 63144, USA

    Google Scholar 

  38. Xie XQ, Chen JZ (2008) Data mining a small molecule drug screening representative subset from NIH PubChem. J Chem Inf Model 48(3):465–475. doi:10.1021/ci700193u

    Article  CAS  PubMed  Google Scholar 

  39. Huffman JW, Yu S, Showalter V et al (1996) Synthesis and pharmacology of a very potent cannabinoid lacking a phenolic hydroxyl with high affinity for the CB2 receptor. J Med Chem 39(20):3875–3877. doi:10.1021/jm960394y

    Article  CAS  PubMed  Google Scholar 

  40. Morgan HL (1965) The generation of a unique machine description for chemical structures—a technique developed at Chemical Abstracts Service. J Chem Doc 5(2):107–113. doi:10.1021/c160017a018

    Article  CAS  Google Scholar 

  41. Gertsch J, Leonti M, Raduner S et al (2008) Beta-caryophyllene is a dietary cannabinoid. Proc Natl Acad Sci 105(26):9099–9104. doi:10.1073/pnas.0803601105

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Raduner S, Majewska A, Chen J-Z et al (2006) Alkylamides from Echinacea are a new class of cannabinomimetics: cannabinoid type 2 receptor-dependent and -independent immunomodulatory effects. J Biol Chem 281(20):14192–14206

    Article  CAS  PubMed  Google Scholar 

  43. Zhang Y, Xie Z, Wang L et al (2011) Mutagenesis and computer modeling studies of a GPCR conserved residue W5.43(194) in ligand recognition and signal transduction for CB2 receptor. Int Immunopharmacol 11(9):1303–1310. doi:10.1016/j.intimp.2011.04.013

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Yang P, Wang L, Feng R et al (2013) Novel triaryl sulfonamide derivatives as selective cannabinoid receptor 2 inverse agonists and osteoclast inhibitors: discovery, optimization, and biological evaluation. J Med Chem 56(5):2045–2058. doi:10.1021/jm3017464

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. DePriest SA, Mayer D, Naylor CB et al (1993) 3D-QSAR of angiotensin-converting enzyme and thermolysin inhibitors: a comparison of CoMFA models based on deduced and experimentally determined active site geometries. J Am Chem Soc 115(13):5372–5384. doi:10.1021/ja00066a004

    Article  CAS  Google Scholar 

  46. Sugimoto H, Tsuchiya Y, Sugumi H et al (1992) Synthesis and structure-activity relationships of acetylcholinesterase inhibitors: 1-benzyl-4-(2-phthalimidoethyl)piperidine, and related derivatives. J Med Chem 35(24):4542–4548. doi:10.1021/jm00102a005

    Article  CAS  PubMed  Google Scholar 

  47. Sugimoto H, Tsuchiya Y, Sugumi H et al (1990) Novel piperidine derivatives. Synthesis and anti-acetylcholinesterase activity of 1-benzyl-4-[2-(N-benzoylamino)ethyl]piperidine derivatives. J Med Chem 33(7):1880–1887. doi:10.1021/jm00169a008

    Article  CAS  PubMed  Google Scholar 

  48. Haefely W, Kyburz E, Gerecke M et al (1985) Recent advances in the molecular pharmacology of benzodiazepine receptors and in the structure-activity relationships of their agonists and antagonists. Adv Drug Res 14:165–322

    CAS  Google Scholar 

  49. Chavatte P, Yous S, Marot C et al (2001) Three-dimensional quantitative structure–activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis. J Med Chem 44(20):3223–3230. doi:10.1021/jm0101343

    Article  CAS  PubMed  Google Scholar 

  50. Talley JJ, Brown DL, Carter JS et al (2000) 4-[5-Methyl-3-phenylisoxazol-4-yl]-benzenesulfonamide, Valdecoxib: a potent and selective inhibitor of COX-2. J Med Chem 43(5):775–777. doi:10.1021/jm990577v

    Article  CAS  PubMed  Google Scholar 

  51. Huang H-C, Li JJ, Garland DJ et al (1996) Diarylspiro[2.4]heptenes as orally active, highly selective cyclooxygenase-2 inhibitors: synthesis and structure–activity relationships. J Med Chem 39(1):253–266. doi:10.1021/jm950664x

    Article  CAS  PubMed  Google Scholar 

  52. Penning TD, Talley JJ, Bertenshaw SR et al (1997) Synthesis and biological evaluation of the 1,5-diarylpyrazole class of cyclooxygenase-2 inhibitors: identification of 4-[5-(4-Methylphenyl)-3-(trifluoromethyl)-1H-pyrazol-1-yl]benzenesulfonamide (SC-58635, Celecoxib). J Med Chem 40(9):1347–1365. doi:10.1021/jm960803q

    Article  CAS  PubMed  Google Scholar 

  53. Li JJ, Norton MB, Reinhard EJ et al (1996) Novel terphenyls as selective cyclooxygenase-2 inhibitors and orally active anti-inflammatory agents. J Med Chem 39(9):1846–1856. doi:10.1021/jm950878e

    Article  CAS  PubMed  Google Scholar 

  54. Li JJ, Anderson GD, Burton EG et al (1995) 1,2-Diarylcyclopentenes as selective cyclooxygenase-2 inhibitors and orally active anti-inflammatory agents. J Med Chem 38(22):4570–4578. doi:10.1021/jm00022a023

    Article  CAS  PubMed  Google Scholar 

  55. Reitz DB, Li JJ, Norton MB et al (1994) Selective cyclooxygenase inhibitors: novel 1,2-diarylcyclopentenes are potent and orally active COX-2 inhibitors. J Med Chem 37(23):3878–3881. doi:10.1021/jm00049a005

    Article  CAS  PubMed  Google Scholar 

  56. Khanna IK, Yu Y, Huff RM et al (2000) Selective cyclooxygenase-2 inhibitors: heteroaryl modified 1,2-diarylimidazoles are potent, orally active antiinflammatory agents. J Med Chem 43(16):3168–3185. doi:10.1021/jm0000719

    Article  CAS  PubMed  Google Scholar 

  57. Khanna IK, Weier RM, Yu Y et al (1997) 1,2-diarylimidazoles as potent, cyclooxygenase-2 selective, and orally active antiinflammatory agents. J Med Chem 40(11):1634–1647. doi:10.1021/jm9700225

    Article  CAS  PubMed  Google Scholar 

  58. Khanna IK, Weier RM, Yu Y et al (1997) 1,2-Diarylpyrroles as potent and selective inhibitors of cyclooxygenase-2. J Med Chem 40(11):1619–1633. doi:10.1021/jm970036a

    Article  CAS  PubMed  Google Scholar 

  59. Gangjee A, Vidwans AP, Vasudevan A et al (1998) Structure-based design and synthesis of lipophilic 2,4-diamino-6-substituted quinazolines and their evaluation as inhibitors of dihydrofolate reductases and potential antitumor agents. J Med Chem 41(18):3426–3434. doi:10.1021/jm980081y

    Article  CAS  PubMed  Google Scholar 

  60. Rosowsky A, Mota CE, Wright JE et al (1994) 2,4-Diamino-5-chloroquinazoline analogs of trimetrexate and piritrexim: synthesis and antifolate activity. J Med Chem 37(26):4522–4528. doi:10.1021/jm00052a011

    Article  CAS  PubMed  Google Scholar 

  61. Rosowsky A, Cody V, Galitsky N et al (1999) Structure-based design of selective inhibitors of dihydrofolate reductase: synthesis and antiparasitic activity of 2,4-diaminopteridine analogues with a bridged diarylamine side chain. J Med Chem 42(23):4853–4860. doi:10.1021/jm990331q

    Article  CAS  PubMed  Google Scholar 

  62. Graffner-Nordberg M, Kolmodin K, Åqvist J et al (2001) Design, synthesis, computational prediction, and biological evaluation of ester soft drugs as inhibitors of dihydrofolate reductase from Pneumocystis carinii. J Med Chem 44(15):2391–2402. doi:10.1021/jm010856u

    Article  CAS  PubMed  Google Scholar 

  63. Gangjee A, Elzein E, Queener SF et al (1998) Synthesis and biological activities of tricyclic conformationally restricted tetrahydropyrido annulated furo[2,3-d]pyrimidines as inhibitors of dihydrofolate reductases. J Med Chem 41(9):1409–1416. doi:10.1021/jm9705420

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. NIDA Center of Excellence for Computational Chemogenomics Drug Abuse Research, Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Kyaw Z. Myint Ph.D. & Xiang-Qun Xie M.B.A., Ph.D.

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  1. Kyaw Z. Myint Ph.D.
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  2. Xiang-Qun Xie M.B.A., Ph.D.
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Correspondence to Kyaw Z. Myint Ph.D. or Xiang-Qun Xie M.B.A., Ph.D. .

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Editors and Affiliations

  1. Chemistry Department, Oxford University, UK, and University of Victoria, Victoria, British Columbia, Canada

    Hugh Cartwright

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Myint, K.Z., Xie, XQ. (2015). Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR). In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_9

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  • DOI: https://doi.org/10.1007/978-1-4939-2239-0_9

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