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Continuous indicator fields: a novel universal type of molecular fields

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Abstract

A novel type of molecular fields, Continuous Indicator Fields (CIFs), is suggested to provide 3D structural description of molecules. The values of CIFs are calculated as the degree to which a point with given 3D coordinates belongs to an atom of a certain type. They can be used similarly to standard physicochemical fields for building 3D structure–activity models. One can build CIF-based 3D structure–activity models in the framework of the continuous molecular fields approach described earlier (J Comput-Aided Mol Des 27 (5):427–442, 2013) for the case of physicochemical molecular fields. CIFs are thought to complement and further extend traditional physicochemical fields. The models built with CIFs can be interpreted in terms of preferable and undesirable positions of certain types of atoms in space. This helps to understand which changes in chemical structure should be made in order to design a compound possessing desirable properties. We have demonstrated that CIFs can be considered as 3D analogues of 2D topological molecular fragments. The performance of this approach is demonstrated in structure–activity studies of thrombin inhibitors, multidentate N-heterocyclic ligands for Am3+/Eu3+ separation, and coloring dyes.

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References

  1. Baskin II, Skvortsova MI, Stankevich IV, Zefirov NS (1995) On the basis of invariants of labeled molecular graphs. J Chem Inf Comput Sci 35(3):527–531. doi:10.1021/ci00025a021

    Article  CAS  Google Scholar 

  2. Varnek A, Fourches D, Hoonakker F, Solov’ev V (2005) Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. J Comput-Aided Mol Des 19(9):693–703. doi:10.1007/s10822-005-9008-0

    Article  CAS  Google Scholar 

  3. Baskin I, Varnek A (2008) Fragment descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening. In: Tropsha A, Varnek A (eds) Chemoinformatics approaches to virtual screening. RSC Publisher, Cambridge, pp 1–43

    Chapter  Google Scholar 

  4. Ruggiu F, Marcou G, Varnek A, Horvath D (2010) ISIDA property-labelled fragment descriptors. Mol Inf 29(12):855–868. doi:10.1002/minf.201000099

    Article  CAS  Google Scholar 

  5. Cruciani G (ed) (2006) Molecular interaction fields; application to drug discovery and ADME prediction. Wiley-VCH, Weinheim

    Google Scholar 

  6. Varnek A, Baskin II (2011) Chemoinformatics as a theoretical chemistry discipline. Mol Inf 30(1):20–32. doi:10.1002/minf.201000100

    Article  CAS  Google Scholar 

  7. Varnek A, Baskin I (2012) Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J Chem Inf Model 52(6):1413–1437. doi:10.1021/ci200409x

    Article  CAS  Google Scholar 

  8. Carbo-Dorca R, Robert D, Amat L, Girones X, Besalu E (2000) Molecular quantum similarity in QSAR and drug design. Lecture notes in chemistry. Springer, Berlin, Heidelberg, New York

    Book  Google Scholar 

  9. Besalu E, Girones X, Amat L, Carbo-Dorca R (2002) Molecular quantum similarity and the fundamentals of QSAR. Acc Chem Res 35(5):289–295. doi:10.1021/ar010048x

    Article  CAS  Google Scholar 

  10. Carbo-Dorca R, Besalu E (2006) Generation of molecular fields, quantum similarity measures and related questions. J Math Chem 39(3–4):495–510. doi:10.1007/s10910-005-9046-9

    Article  CAS  Google Scholar 

  11. Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28(7):849–857. doi:10.1021/jm00145a002

    Article  CAS  Google Scholar 

  12. Goodford P (2006) The basic principles of GRID. In: Cruciani G (ed) Molecular interaction fields. Applications in drug discovery and ADME prediction. Methods and principles in medicinal chemistry, vol 27. Wiley-VCH, Weinheim, pp 3–26

    Google Scholar 

  13. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37(24):4130–4146. doi:10.1021/jm00050a010

    Article  CAS  Google Scholar 

  14. Kellogg GE (1996) E-state fields: applications to 3D QSAR. J Comput-Aided Mol Des 10(6):513–520. doi:10.1007/BF00134175

    Article  CAS  Google Scholar 

  15. Totrov M (2008) Atomic property fields: generalized 3D pharmacophoric potential for automated ligand superposition, pharmacophore elucidation and 3D QSAR. Chem Biol Drug Des 71(1):15–27. doi:10.1111/j.1747-0285.2007.00605.x

    Article  CAS  Google Scholar 

  16. Kroemer RT, Hecht P (1995) Replacement of steric 6–12 potential-derived interaction energies by atom-based indicator variables in CoMFA leads to models of higher consistency. J Comput-Aided Mol Des 9(3):205–212. doi:10.1007/BF00124452

    Article  CAS  Google Scholar 

  17. Zhokhova NI, Baskin II, Bakhronov DK, Palyulin VA, Zefirov NS (2009) Method of continuous molecular fields in the search for quantitative structure-activity relationships. Dokl Chem 429(1):273–276. doi:10.1134/S0012500809110056

    Article  CAS  Google Scholar 

  18. Baskin II, Zhokhova NI (2013) The continuous molecular fields approach to building 3D-QSAR models. J Comput-Aided Mol Des 27(5):427–442. doi:10.1007/s10822-013-9656-4

    Article  CAS  Google Scholar 

  19. Karpov PV, Baskin II, Zhokhova NI, Zefirov NS (2011) Method of continuous molecular fields in the one-class classification task. Dokl Chem 440(2):263–265. doi:10.1134/s0012500811100016

    Article  CAS  Google Scholar 

  20. Karpov PV, Baskin II, Zhokhova NI, Nawrozkij MB, Zefirov AN, Yablokov AS, Novakov IA, Zefirov NS (2011) One-class approach: models for virtual screening of non-nucleoside HIV-1 reverse transcriptase inhibitors based on the concept of continuous molecular fields. Russ Chem Bull 60(11):2418–2424. doi:10.1007/s11172-011-0372-8

    Article  CAS  Google Scholar 

  21. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222. doi:10.1023/B:STCO.0000035301.49549.88

    Article  Google Scholar 

  22. Bennett KP, Embrechts MJ (2003) An optimization perspective on Kernel partial least squares regression. In: Suykens JAK, Horvath G, Basu S, Micchelli C, Vandewalle J (eds) Advances in learning theory: methods models and applications. NATO science series III: computer & systems sciences, vol 190. IOS Press, Amsterdam, pp 227–250

    Google Scholar 

  23. Rasmussen CE, Williams CKI (2006) Gaussian processes in machine learning. adaptive computation and machine learning. The MIT Press, Cambridge

    Google Scholar 

  24. Smola AJ, Scholkopf B, Muller KR (1998) The connection between regularization operators and support vector kernels. Neural Netw 11(4):637–649. doi:10.1016/s0893-6080(98)00032-x

    Article  Google Scholar 

  25. Artemenko NV, Baskin II, Palyulin VA, Zefirov NS (2001) Prediction of physical properties of organic compounds using artificial neural networks within the substructure approach. Dokl Chem 381(1–3):317–320. doi:10.1023/A:1012976623974

    Article  Google Scholar 

  26. Artemenko NV, Baskin II, Palyulin VA, Zefirov NS (2003) Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds. Russ Chem Bull 52(1):20–29. doi:10.1023/A:1022467508832

    Article  CAS  Google Scholar 

  27. Baskin I, Varnek A (2008) Building a chemical space based on fragment descriptors. Comb Chem High Throughput Screen 11(8):661–668. doi:10.2174/138620708785739907

    Article  CAS  Google Scholar 

  28. Zhokhova NI, Baskin II, Palyulin VA, Zefirov AN, Zefirov NS (2007) Fragmental descriptors with labeled atoms and their application in QSAR/QSPR studies. Dokl Chem 417(2):282–284. doi:10.1134/S0012500807120026

    Article  CAS  Google Scholar 

  29. Clark M, Cramer RD, Vanopdenbosch N (1989) Validation of the general-puppose TRIPOS 5.2 force-field. J Comp Chem 10(8):982–1012. doi:10.1002/jcc.540100804

    Article  CAS  Google Scholar 

  30. Böhm M, Stüjrzebecher J, Klebe G (1999) Three-dimensional quantitative structure-activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. J Med Chem 42(3):458–477. doi:10.1021/jm981062r

    Article  Google Scholar 

  31. Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A (2008) Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48(9):1733–1746. doi:10.1021/ci800151m

    Article  CAS  Google Scholar 

  32. Ward MD (ed) (2003) Comprehensive coordination chemistry II. Applications of coordination chemistry, vol 9. Elsevier, San Diego

    Google Scholar 

  33. Gordon JC, Czerwinski K, Francesconi L (2013) Preface: forum on aspects of inorganic chemistry related to nuclear energy. Inorg Chem 52(7):3405–3406. doi:10.1021/ic4006136

    Article  CAS  Google Scholar 

  34. Gorden AEV, DeVore MA, Maynard BA (2013) Coordination chemistry with f-element complexes for an improved understanding of factors that contribute to extraction selectivity. Inorg Chem 52(7):3445–3458. doi:10.1021/ic300887p

    Article  CAS  Google Scholar 

  35. Hudson MJ, Harwood LM, Laventine DM, Lewis FW (2013) Use of soft heterocyclic N-donor ligands to separate actinides and lanthanides. Inorg Chem 52(7):3414–3428. doi:10.1021/ic3008848

    Article  CAS  Google Scholar 

  36. Whittaker DM, Griffiths TL, Helliwell M, Swinburne AN, Natrajan LS, Lewis FW, Harwood LM, Parry SA, Sharrad CA (2013) Lanthanide Speciation In Potential SANEX and GANEX actinide/lanthanide separations using tetra-N-donor extractants. Inorg Chem 52(7):3429–3444. doi:10.1021/ic301599y

    Article  CAS  Google Scholar 

  37. Pearson RG (1963) Hard and soft acids and bases. J Am Chem Soc 85(22):3533–3539. doi:10.1021/ja00905a001

    Article  CAS  Google Scholar 

  38. Kolarik Z (2008) Complexation and separation of lanthanides(III) and actinides(III) by heterocyclic N-donors in solutions. Chem Rev 108(10):4208–4252. doi:10.1021/cr078003i

    Article  CAS  Google Scholar 

  39. Varnek A, Fourches D, Sieffert N, Solov’ev VP, Hill C, Lecomte M (2007) QSPR modeling of the Am-III/Eu-III separation factor: how far can we predict. Solvent Extr Ion Exc 25(1):1–26. doi:10.1080/07366290601067481

    Article  CAS  Google Scholar 

  40. ChemAxon (2012) ChemAxon Kft., Záhony u. 7, Building HX, 1031 Budapest, Hungary. http://www.chemaxon.com

  41. Arun KS, Huang TS, Blostein SD (1987) Least square fitting of two 3-D point sets. IEEE Trans Pattern Anal PAMI 9(5):698–700. doi:10.1109/TPAMI.1987.4767965

    Article  CAS  Google Scholar 

  42. Drew MGB, Hudson MJ, Youngs TGA (2004) QSAR studies of multidentate nitrogen ligands used in lanthanide and actinide extraction processes. J Alloy Compd 374(1–2):408–415. doi:10.1016/j.jallcom.2003.11.047

    Article  CAS  Google Scholar 

  43. Luan F, Xu X, Liu H, Cordeiro MNDS (2013) Review of quantitative structure activity/property relationship studies of dyes: recent advances and perspectives. Color Technol 129(3):173–186. doi:10.1111/cote.12027

    Article  CAS  Google Scholar 

  44. Timofei S, Schmidt W, Kurunczi L, Simon Z (2000) A review of QSAR for dye affinity for cellulose fibres. Dyes Pigment 47(1–2):5–16. doi:10.1016/S0143-7208(00)00058-9

    Article  CAS  Google Scholar 

  45. Timofei S, Fabian WMF (1998) Comparative molecular field analysis of heterocyclic monoazo dye-fiber affinities. J Chem Inf Comput Sci 38(6):1218–1222. doi:10.1021/ci9704367

    Article  CAS  Google Scholar 

  46. Fabian WMF, Timofei S, Kurunczi L (1995) Comparative molecular field analysis (CoMFA), semiempirical (AM1) molecular orbital and multiconformational minimal steric difference (MTD) calculations of anthraquinone dye-fibre affinities. J Mol Struct - THEOCHEM 340(1–3):73–81. doi:10.1016/0166-1280(95)04163-Z

    Article  CAS  Google Scholar 

  47. Funar-Timofei S, Schueuermann G (2002) Comparative molecular field analysis (CoMFA) of anionic azo dye-fiber affinities I: gas-phase molecular orbital descriptors. J Chem Inf Comput Sci 42(4):788–795. doi:10.1021/ci010086v

    Article  CAS  Google Scholar 

  48. Timofei S, Kurunczi L, Schmidt W, Simon Z (2002) Steric and electrostatic effects in dye-cellulose interactions by the MTD and CoMFA approaches. SAR QSAR Environ Res 13(2):219–226. doi:10.1080/10629360290002703

    Article  CAS  Google Scholar 

  49. Schuurmann G, Funar-Timofei S (2003) Multilinear regression and comparative molecular field analysis (CoMFA) of Azo dye-fiber affinities. 2. Inclusion of solution-phase molecular orbital descriptors. J Chem Inf Comput Sci 43(5):1502–1512. doi:10.1021/ci034064f

    Article  Google Scholar 

  50. Polanski J, Gieleciak R, Wyszomirski M (2003) Comparative molecular surface analysis (CoMSA) for modeling dye-fiber affinities of the azo and anthraquinone dyes. J Chem Inf Comput Sci 43(6):1754–1762. doi:10.1021/ci0340761

    Article  CAS  Google Scholar 

  51. Polanski J, Gieleciak R, Wyszomirski M (2004) Mapping dye pharmacophores by the comparative molecular surface analysis (CoMSA): application to heterocyclic monoazo dyes. Dyes Pigment 62(1):61–76. doi:10.1016/j.dyepig.2003.11.003

    Article  CAS  Google Scholar 

  52. Gilardi RD, Karle IL (1972) The crystal structure of 4-phenylazoazobenzene. Acta Crystallogr Sect B: Struct Sci 28(5):1635–1638. doi:10.1107/S056774087200473X

    Article  CAS  Google Scholar 

  53. Hanson A (1973) The crystal structure of methyl orange monohydrate monoethanolate. Acta Crystallogr Sect B: Struct Sci 29(3):454–460. doi:10.1107/S0567740873002748

    Article  CAS  Google Scholar 

  54. Glen RC, Bender A, Arnby CH, Carlsson L, Boyer S, Smith J (2006) Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME. IDrugs 9(3):199–204

    CAS  Google Scholar 

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Acknowledgments

This work was supported by Russian Foundation for Basic Research (Grant 13-07-00511).The work is performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

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Authors and Affiliations

  1. A.N.Nesmeyanov Institute of Organoelement Compounds of Russian Academy of Sciences, Vavilova St. 28, 119991, Moscow, Russia

    Gleb V. Sitnikov

  2. Faculty of Physics, M.V.Lomonosov Moscow State University, Leninskie Gory, 119991, Moscow, Russia

    Nelly I. Zhokhova & Igor I. Baskin

  3. Department of Chemistry, M.V.Lomonosov Moscow State University, Leninskie Gory, 119991, Moscow, Russia

    Yury A. Ustynyuk

  4. Laboratoire de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, 1 rue Blaise Pascal, 67000, Strasbourg, France

    Gleb V. Sitnikov & Alexandre Varnek

  5. Kazan (Volga River) Federal University, ul. Kremlevskaya 18, 420008, Kazan, Tatarstan, Russia

    Igor I. Baskin

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  1. Gleb V. Sitnikov
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  2. Nelly I. Zhokhova
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Correspondence to Igor I. Baskin.

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Sitnikov, G.V., Zhokhova, N.I., Ustynyuk, Y.A. et al. Continuous indicator fields: a novel universal type of molecular fields. J Comput Aided Mol Des 29, 233–247 (2015). https://doi.org/10.1007/s10822-014-9818-z

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