This work was partially supported by the Russian Foundation for Basic Research (projects 14-03-01063 and 15-03-09084). The authors are grateful to the ChemAxon and OpenEye companies for kindly providing the academic licenses for the structural data processing and molecular modeling software used in preparing the illustrations.


  • 1. M. I. Skvortsova, I. V. Stankevich, V. A. Palyulin and N. S. Zefirov, Molecular similarity concept and its use for predicting the properties of chemical compounds, Russ. Chem. Rev., 2006, 75(11), 961-979, DOI: 10.1070/ RC2006v075n11ABEH003616.
  • 2. Concepts and Applications of Molecular Similarity, ed. M. A. Johnson and G. M. Maggiora, Wiley, 1990, ISBN: 978-0-471-62175-1.
  • 3. D. F. Larder and F. F. Kluge, Alexander Mikhailovich Butlerov’s theory of chemical structure, J. Chem. Educ., 1971, 48(5), 287-289, DOI: 10.1021/ ed048p287.
  • 4. D. E. Lewis, 150 Years of organic structures, in Atoms in Chemistry: From Dalton’s Predecessors to Complex Atoms and Beyond, ed. C. J. Giunta, ACS, 2010, pp. 35-57, DOI: 10.1021/bk-2010-1044.ch004.
  • 5. J. L. Medina-Franco and G. M. Maggiora, Molecular Similarity Analysis, in Chemoinformatics for Drug Discovery, ed. J. Bajorath, Wiley, 2014, pp. 343-399, DOI: 10.1002/9781118742785.ch15.
  • 6. G. Maggiora, M. Vogt, D. Stumpfe and J. Bajorath, Molecular similarity in medicinal chemistry, J. Med. Chem., 2014, 57(8), 3186-3204, DOI: 10.1021/jm401411z.
  • 7. Recent Advances in QSAR Studies: Methods and Applications, ed. T. Puzyn, J. Leszczynski and M. T. Cronin, Springer, 2010, DOI: 10.1007/978-
  • 1-4020-9783-6.
  • 8. Computational Toxicology, ed. B. Reisfeld and A. Mayeno, Springer, 2013, vol. I, DOI: 10.1007/978-1-62703-050-2.
  • 9. Computational Toxicology, ed. B. Reisfeld and A. Mayeno, Springer, 2013, vol. II, DOI: 10.1007/978-1-62703-059-5.
  • 10. S. Ekins, S. Kortagere, M. D. Krasowski, A. J. Williams, J. J. Xu and M. Zientek, Ligand-based modeling of toxicity, in Drug Design Strategies: Quantitative Approaches, ed. D. J. Livingstone and A. M. Davis, RSC, 2012, pp. 312-344, DOI: 10.1039/9781849733410-00312.
  • 11. A. M. Davis and R. J. Riley, ADME(T) predictions in drug discovery, in Drug Design Strategies: Quantitative Approaches, ed. D. J. Livingstone and A. M. Davis, RSC, 2012, pp. 345-366, DOI: 10.1039/9781849733410-00345.
  • 12. T. Steger-Hartmann, In silico toxicology - current approaches and future perspectives to predict toxic effects with computational tools, in Predictive Toxicology: From Vision to Reality, ed. F. Pfannkuch and L. Suter- Dick, Wiley, 2015, pp. 11-32, DOI: 10.1002/9783527674183.ch02.
  • 13. Y. Hu, D. Stumpfe and J. Bajorath, Advancing the activity cliff concept, F1000Research, 2013, 2, 199, DOI: 10.12688/f1000research.2-199.v1.
  • 14. A. Dalke, Molecular fingerprints background, Dalke Scientific Software, LLC, 2008, 06/26/fingerprint_background.html, accessed 01.06.2016.
  • 15. Fingerprints - screening and similarity, Daylight theory manual, Daylight 4.9, Daylight Chemical Information Systems, Inc., 2011, http://, accessed 01.06.2016.
  • 16. MACCS Structural Keys, ISIS/Base 2.4, Accelrys, San Diego, CA, 2002.
  • 17. PubChem Substructure Fingerprint V1.3, chem/specifications/pubchem_fingerprints.txt, accessed 01.06.2016.
  • 18. D. Rogers and M. Hahn, Extended-connectivity fingerprints, J. Chem. Inf. Model., 2010, 50(5), 742-754, DOI: 10.1021/ci100050t.
  • 19. Chemical Hashed Fingerprint, ChemAxon Ltd, https://docs.chemaxon. com/display/docs/Chemical+Hashed+Fingerprint, accessed 01.06.2016.
  • 20. J. L. Durant, B. A. Leland, D. R. Henry and J. G. Nourse, Reoptimization of MDL keys for use in drug discovery, J. Chem. Inf. Comput. Sci., 2002, 42(6), 1273-1280, DOI: 10.1021/ci010132r.
  • 21. K. Heikamp and J. Bajorat, Fingerprint design and engineering strategies: rationalizing and improving similarity search performance, Future Med. Chem., 2012, 4(15), 1945-1959, DOI: 10.4155/fmc.12.126.
  • 22. S. Jasial, Y. Hu, M. Vogt and J. Bajorath, Activity-relevant similarity values for fingerprints and implications for similarity searching, F1000- Research, 2016, 5, 591, DOI: 10.12688/f1000research.8357.2.
  • 23. D. Livingstone, A Practical Guide to Scientific Data Analysis, Wiley, 2009, DOI: 10.1002/9780470017913.
  • 24. D. I. Osolodkin, E. V. Radchenko, A. A. Orlov, A. E. Voronkov, V. A. Pal- yulin and N. S. Zefirov, Progress in visual representations of chemical space, Expert Opin. Drug Discovery, 2015, 10(9), 959-973, DOI: 10.1517/17460441.2015.1060216.
  • 25. R. D. Cramer, D. E. Patterson and J. D. Bunce, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc., 1988, 110(18), 5959-5967, DOI: 10.1021/ ja00226a005.
  • 26. G. Klebe, U. Abraham and T. Mietzner, Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity,J. Med. Chem., 1994, 37(24), 4130-4146, DOI: 10.1021/jm00050a010.
  • 27. J. A. Grant, M. A. Gallardo and B. T. Pickup, A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape, J. Comput. Chem., 1996, 17(14), 1653-1666, DOI: 10.1002/(SICI)1096-987X(19961115)17:14<1653::AID-JCC7>3.0.CO;2-K.
  • 28. P. C. D. Hawkins, A. G. Skillman and A. Nicholls, Comparison of shape-matching and docking as virtual screening tools, J. Med. Chem., 2007, 50(1), 74-82, DOI: 10.1021/jm0603365.
  • 29. ROCS Theory, OpenEye Scientific Software, Santa Fe, NM, https://docs., accessed 01.06.2016.
  • 30. V. A. Palyulin, E. V. Radchenko and N. S. Zefirov, Molecular Field Topology Analysis method in QSAR studies of organic compounds, J. Chem. Inf. Comput. Sci., 2000, 40(3), 659-667, DOI: 10.1021/ci980114i.
  • 31. E. V. Radchenko, V. A. Palyulin and N. S. Zefirov, Molecular Field Topology Analysis in drug design and virtual screening, in Chemoinformatics Approaches to Virtual Screening, ed. A. Varnek and A. Tropsha, RSC, 2008, pp. 150-181, DOI: 10.1039/9781847558879-00150.
  • 32. P. Willett, J. M. Barnard and G. M. Downs, Chemical similarity searching, J. Chem. Inf. Comput. Sci., 1998, 38(6), 983-996, DOI: 10.1021/ ci9800211.
  • 33. S. J. Swamidass and P. Baldi, Bounds and algorithms for fast exact searches of chemical fingerprints in linear and sublinear time, J. Chem. Inf. Model., 2007, 47(2), 302-317, DOI: 10.1021/ci600358f.
  • 34. J. W. Raymond, E. J. Gardiner and P. Willett, Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm, J. Chem. Inf. Comput. Sci., 2002, 42(2), 305-316, DOI: 10.1021/ci010381f.
  • 35. Y. Cao, T. Jiang and T. Girke, A maximum common substructure-based algorithm for searching and predicting drug-like compounds, Bioinformatics, 2008, 24(13), i366-i374, DOI: 10.1093/bioinformatics/btn186.
  • 36. J. J. McGregor and P. Willett, Use of a maximum common subgraph algorithm in the automatic identification of ostensible bond changes occurring in chemical reactions, J. Chem. Inf. Comput. Sci., 1981, 21(3), 137-140, DOI: 10.1021/ci00031a005.
  • 37. G. M. Maggiora, J. D. Petke and J. Mestres, A general analysis of field-based molecular similarity indices, J. Math. Chem., 2002, 31(3), 251-270, DOI: 10.1023/A:1020784004649.
  • 38. Y. Wang and J. Bajorath, Balancing the influence of molecular complexity on fingerprint similarity searching,J. Chem. Inf. Model., 2008, 48(1), 7584, DOI: 10.1021/ci700314x.
  • 39. J. Mestres and G. M. Maggiora, Putting molecular similarity into context: asymmetric indices for field-based similarity measures, J. Math. Chem., 2006, 39(1), 107-118, DOI: 10.1007/s10910-005-9007-3.
  • 40. A. Tversky, Features of similarity, Psychol. Rev., 1977, 84(4), 327-352, DOI: 10.1037/0033-295X.84.4.327.
  • 41. X. Chen and F. K. Brown, Asymmetry of chemical similarity, ChemMed- Chem, 2007, 2(2), 180-182, DOI: 10.1002/cmdc.200600161.
  • 42. A. Mauri, D. Ballabio, R. Todeschini and V. Consonni, Mixtures, metabolites, ionic liquids: A new measure to evaluate similarity between complex chemical systems, 21st European Symposium on Quantitative Structure-Activity Relationship (EuroQSAR 2016), Verona, Italy, September 4-8, 2016, p. OC20.
  • 43. Instant JChem 16.5.23, ChemAxon Ltd, 2016,
  • 44. MFTAWin 3.5, Department of Chemistry, Lomonosov Moscow State University, 2015,
  • 45. PubChem Compound Database, National Center for Biotechnology Information, CID 6175,, accessed 01.06.2016.
  • 46. PubChem Compound Database, National Center for Biotechnology Information, CID 73339, pound/73339, accessed 01.06.2016.
  • 47. R. Guha, Exploring structure-activity data using the landscape paradigm, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 2012, 2(6), 829-841, DOI: 10.1002/wcms.1087.
  • 48. G. M. Maggiora, On outliers and activity cliffs - Why QSAR often disappoints, J. Chem. Inf. Model., 2006, 46(4), 1535, DOI: 10.1021/ci060117s.
  • 49. M. Cruz-Monteagudo, J. L. Medina-Franco, Y. Perez-Castillo, O. Nico- lotti, M. N. Cordeiro and F. Borges, Activity cliffs in drug discovery: Dr Jekyll or Mr Hyde? Drug Discovery Today, 2014, 19(8), 1069-1080, DOI: 10.1016/j.drudis.2014.02.003.
  • 50. J. L. Medina-Franco, G. Navarrete-Vazquez and O. Mendez-Lucio, Activity and property landscape modeling is at the interface of chemoinformat- ics and medicinal chemistry, Future Med. Chem., 2015, 7(9), 1197-1211, DOI: 10.4155/fmc.15.51.
  • 51. D. Stumpfe and J. Bajorath, Exploring activity cliffs in medicinal chemistry, J. Med. Chem., 2012, 55(7), 2932-2942, DOI: 10.1021/jm201706b.
  • 52. D. Dimova and J. Bajorath, Advances in activity cliff research, Mol. Inf., 2016, 35(5), 181-191, doI: 10.1002/minf.201600023.
  • 53. H. Schonherr and T. Cernak, Profound methyl effects in drug discovery and a call for new C-H methylation reactions, Angew. Chem., Int. Ed., 2013, 52(47), 12256-12267, DOI: 10.1002/anie.201303207.
  • 54. P. J. Coleman, J. D. Schreier, C. D. Cox, M. J. Breslin, D. B. Whitman,

M. J. Bogusky, G. B. McGaughey, R. A. Bednar, W. Lemaire, S. M. Doran,

S. V. Fox, S. L. Garson, A. L. Gotter, C. M. Harrell, D. R. Reiss, T. D. Cabalu, D. Cui, T. Prueksaritanont, J. Stevens, P. L. Tannenbaum, R. G. Ball,

J. Stellabott, S. D. Young, G. D. Hartman, C. J. Winrow and J. J. Renger, Discovery of [(2R,5R)-5-{[(5-fluoropyridin-2-yl)oxy]methyl}-2-methylpiper- idin-1-yl][5-methyl-2-(pyrimidin-2-yl)phenyl]methanone (MK-6096): A dual orexin receptor antagonist with potent sleep-promoting properties, ChemMedChem, 2012, 7(3), 415-424, DOI: 10.1002/cmdc.201200025.

55. K. W. Kuntz, J. E. Campbell, H. Keilhack, R. M. Pollock, S. K. Knutson,

M. Porter-Scott, V. M. Richon, C. J. Sneeringer, T. J. Wigle, C. J. Allain,

C. R. Majer, M. P. Moyer, R. A. Copeland and R. Chesworth, The importance of being Me: Magic methyls, methyltransferase inhibitors, and the discovery of tazemetostat, J. Med. Chem., 2016, 59(4), 1556-1564, DOI: 10.1021/acs.jmedchem.5b01501.

  • 56. M. R. Wood, C. R. Hopkins, J. T. Brogan, P. J. Conn and C. W. Lindsley, “Molecular switches” on mGluR allosteric ligands that modulate modes of pharmacology, Biochemistry, 2011, 50(13), 2403-2410, DOI: 10.1021/ bi200129s.
  • 57. S. Purser, P. R. Moore, S. Swallow and V. Gouverneur, Fluorine in medicinal chemistry, Chem. Soc. Rev., 2008, 37(2), 320-330, DOI: 10.1039/ b610213c.
  • 58. E. P. Gillis, K. J. Eastman, M. D. Hill, D. J. Donnelly and N. A. Meanwell, Applications of fluorine in medicinal chemistry, J. Med. Chem., 2015, 58(21), 8315-8359, DOI: 10.1021/acs.jmedchem.5b00258.
  • 59. S.-F. Lu, B. Herbert, G. Haufe, K. W. Laue, W. L. Padgett, O. Oshunleti, J.

W. Daly and K. L. Kirk, Syntheses of (R)- and (S)-2- and 6-fluoronorepi- nephrine and (R)- and (S)-2- and 6-fluoroepinephrine: Effect of stereochemistry on fluorine-induced adrenergic selectivities, J. Med. Chem., 2000, 43(8), 1611-1619, DOI: 10.1021/jm990599h.

60. M. J. Shaughnessy, A. Harsanyi, J. Li, T. Bright, C. D. Murphy and

G. Sandford, Targeted fluorination of a nonsteroidal anti-inflammatory drug to prolong metabolic half-life, ChemMedChem, 2014, 9(4), 733-736, DOI: 10.1002/cmdc.201300490.

  • 61. D. Stumpfe, Y. Hu, D. Dimova and J. Bajorath, Recent progress in understanding activity cliffs and their utility in medicinal chemistry, J. Med. Chem., 2014, 57(1), 18-28, DOI: 10.1021/jm401120g.
  • 62. D. Dimova, D. Stumpfe, Y. Hu and J. Bajorath, Activity cliff clusters as a source of structure-activity relationship information, Expert Opin. Drug Discovery, 2015, 10(5), 441-447, DOI: 10.1517/17460441.2015.1019861.
  • 63. R. Guha and J. H. Van Drie, Structure-Activity Landscape Index: Identifying and quantifying activity cliffs, J. Chem. Inf. Model., 2008, 48(3), 646-658, DOI: 10.1021/ci7004093.
  • 64. R. Guha, The ups and downs of structure-activity landscapes, in Chemoinformatics and Computational Chemical Biology, ed. J. Bajorath, Springer, 2011, pp. 101-117, DOI: 10.1007/978-1-60761-839-3_3.
  • 65. R. Guha and J. H. Van Drie, Assessing how well a modeling protocol captures a structure-activity landscape,J. Chem. Inf. Model., 2008, 48(8), 1716-1728, DOI: 10.1021/ci8001414.
  • 66. R. Guha and J. L. Medina-Franco, On the validity versus utility of activity landscapes: are all activity cliffs statistically significant? J. Cheminf., 2014, 6(1), 11, DOI: 10.1186/1758-2946-6-11.
  • 67. L. Peltason and J. Bajorath, SAR Index: Quantifying the nature of structure-activity relationships, J. Med. Chem., 2007, 50(23), 5571-5578, DOI: 10.1021/jm0705713.
  • 68. X. Hu, Y. Hu, M. Vogt, D. Stumpfe and J. Bajorath, MMP-Cliffs: Systematic identification of activity cliffs on the basis of matched molecular pairs, J. Chem. Inf. Model., 2012, 52(5), 1138-1145, DOI: 10.1021/ ci3001138.
  • 69. J. L. Medina-Franco, Activity cliffs: facts or artifacts?, Chem. Biol. Drug Des., 2013, 81(5), 553-556, DOI: 10.1111/cbdd.12115.
  • 70. K. Heikamp, X. Hu, A. Yan and J. Bajorath, Prediction of activity cliffs using support vector machines, J. Chem. Inf. Model., 2012, 52(9), 2354-2365, DOI: 10.1021/ci300306a.
  • 71. C. Tebby and E. Mombelli, Modelling structure activity landscapes with cliffs: A kernel regression-based approach, Mol. Inf., 2013, 32(7), 609-623, DOI: 10.1002/minf.201300016.
  • 72. J. Husby, G. Bottegoni, I. Kufareva, R. Abagyan and A. Cavalli, Structure-based predictions of activity cliffs,J. Chem. Inf. Model., 2015, 55(5), 1062-1076, DOI: 10.1021/ci500742b.
  • 73. K. Klimenko, G. Marcou, D. Horvath and A. Varnek, Chemical space mapping and structure-activity analysis of the ChEMBL antiviral compound set, J. Chem. Inf. Model., 2016, 56(8), 1438-1454, DOI: 10.1021/ acs.jcim.6b00192.
  • 74. Y. Hu, G. M. Maggiora and J. Bajorath, Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account, J. Comput.-Aided Mol. Des., 2013, 27(2), 115-124, DOI: 10.1007/s10822- 012-9632-4.
  • 75. D. Stumpfe and J. Bajorath, Frequency of occurrence and potency range distribution of activity cliffs in bioactive compounds, J. Chem. Inf. Model., 2012, 52(9), 2348-2353, DOI: 10.1021/ci300288f.
  • 76. Y. Hu and J. Bajorath, Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database, J. Chem. Inf. Model., 2012, 52(7), 1806-1811, DOI: 10.1021/ci300274c.
  • 77. Y. Hu and J. Bajorath, Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2d and 3d cliffs,J. Chem. Inf. Model., 2012, 52(3), 670-677, DOI: 10.1021/ci300033e.
  • 78. A. Gaulton, L. J. Bellis, A. P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani and J. P. Over- ington, ChEMBL: a large-scale bioactivity database for drug discovery, NucleicAcids Res., 2012, 40, D1100-D1107, DOI: 10.1093/nar/gkr777.
  • 79. D. Stumpfe, A. de la Vega de Leon, D. Dimova and J. Bajorath, Advancing the activity cliff concept, part II, F1000Research, 2014, 3, 75, DOI: 10.12688/f1000research.3788.1.
  • 80. D. Dimova, D. Stumpfe and J. Bajorath, Method for the evaluation of structure-activity relationship information associated with coordinated activity cliffs, J. Med. Chem., 2014, 57(15), 6553-6563, DOI: 10.1021/ jm500577n.
  • 81. D. Stumpfe, D. Dimova, K. Heikamp and J. Bajorath, Compound pathway model to capture SAR progression: Comparison of activity cliff- dependent and -independent pathways, J. Chem. Inf. Model., 2013, 53(5), 1067-1072, DOI: 10.1021/ci400141w.
  • 82. D. Dimova, K. Heikamp, D. Stumpfe and J. Bajorath, Do medicinal chemists learn from activity cliffs? A systematic evaluation of cliff progression in evolving compound data sets, J. Med. Chem., 2013, 56(8), 3339-3345, DOI: 10.1021/jm400147j.
  • 83. Virtual Screening: Principles, Challenges, and Practical Guidelines, ed. C. Sotriffer, Wiley, 2011, ISBN: 978-3-527-32636-5.
  • 84. D. Stumpfe and J. Bajorath, Similarity searching, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 2011, 1(2), 260-282, DOI: 10.1002/wcms.23.
  • 85. I. Muegge and P. Mukherjee, An overview of molecular fingerprint similarity search in virtual screening, Expert Opin. Drug Discovery, 2016, 11(2), 137-148, DOI: 10.1517/17460441.2016.1117070.
  • 86. D. E. Patterson, R. D. Cramer, A. M. Ferguson, R. D. Clark and L. E. Weinberger, Neighborhood behavior: A useful concept for validation of “molecular diversity” descriptors,J. Med. Chem., 1996, 39(16), 30493059, DOI: 10.1021/jm960290n.
  • 87. T. Girschick, L. Puchbauer and S. Kramer, Improving structural similarity based virtual screening using background knowledge, J. Cheminf., 2013, 5(1), 50, DOI: 10.1186/1758-2946-5-50.
  • 88. R. Todeschini, D. Ballabio, M. Cassotti and V. Consonni, N3 and BNN: Two new similarity based classification methods in comparison with other classifiers, J. Chem. Inf. Model., 2015, 55(11), 2365-2374, DOI: 10.1021/acs.jcim.5b00326.
  • 89. M. Luo, X. S. Wang and A. Tropsha, Comparative analysis of QSAR-based vs. chemical similarity based predictors of GPCRs binding affinity, Mol. Inf., 2016, 35(1), 36-41, DOI: 10.1002/minf.201500038.
  • 90. M. J. Yu, Predicting total clearance in humans from chemical structure, J. Chem. Inf. Model., 2010, 50(7), 1284-1295, DOI: 10.1021/ci1000295.
  • 91. M. Cassotti, D. Ballabio, R. Todeschini and V. Consonni, A similarity- based QSAR model for predicting acute toxicity towards the fathead minnow (pimephales promelas), SAR QSAR Environ. Res., 2015, 26(3), 217-243, DOI: 10.1080/1062936X.2015.1018938.
  • 92. T. W. Schultz, P. Amcoff, E. Berggren, F. Gautier, M. Klaric, D. J. Knight, C. Mahony, M. Schwarz, A. White and M. T. D. Cronin, A strategy for structuring and reporting a read-across prediction of toxicity, Regul. Toxicol. Pharmacol., 2015, 72(3), 586-601, DOI: 10.1016/j.yrtph.2015.05.016.
  • 93. M. Cronin, J. Madden, S. Enoch and D. Roberts, Chemical Toxicity Prediction: Category Formation and Read-Across, RSC, 2013, DOI: 10.1039/9781849734400, ISBN: 978-1-84973-384-7.
  • 94. M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin and B.

K. Shoichet, Relating protein pharmacology by ligand chemistry, Nat. Biotechnol., 2007, 25(2), 197-206, DOI: 10.1038/nbt1284.

  • 95. J. Hert, M. J. Keiser, J. J. Irwin, T. I. Oprea and B. K. Shoichet, Quantifying the relationships among drug classes, J. Chem. Inf. Model., 2008, 48(4), 755-765, DOI: 10.1021/ci8000259.
  • 96. Z. Wang, L. Liang, Z. Yin and J. Lin, Improving chemical similarity ensemble approach in target prediction, J. Cheminf., 2016, 8, 20, DOI: 10.1186/s13321-016-0130-x.
  • 97. F. Schmidt, H. Matter, G. Hessler and A. Czich, Predictive in silico off- target profiling in drug discovery, Future Med. Chem., 2014, 6(3), 295-317, DOI: 10.4155/fmc.13.202.
  • 98. M. Schenone, V. Dancik, B. K. Wagner and P. A. Clemons, Target identification and mechanism of action in chemical biology and drug discovery, Nat. Chem. Biol., 2013, 9(4), 232-240, DOI: 10.1038/ nchembio.1199.
  • 99. M. J. Keiser, V. Setola, J. J. Irwin, C. Laggner, A. I. Abbas, S. J. Hufeisen,

N. H. Jensen, M. B. Kuijer, R. C. Matos, T. B. Tran, R. Whaley, R. A. Glennon, J. Hert, K. L. Thomas, D. D. Edwards, B. K. Shoichet and B. L. Roth, Predicting new molecular targets for known drugs, Nature, 2009, 462(7270), 175-181, DOI: 10.1038/nature08506.

  • 100. A. J. DeGraw, M. J. Keiser, J. D. Ochocki, B. K. Shoichet and M. D. Diste- fano, Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs,J. Med. Chem., 2010, 53(6), 2464-2471, DOI: 10.1021/jm901613f.
  • 101. C. Laggner, D. Kokel, V. Setola, A. Tolia, H. Lin, J. J. Irwin, M. J. Keiser, C. Y. Cheung, D. L. Minor, B. L. Roth, R. T. Peterson and B. K. Shoichet, Chemical informatics and target identification in a zebrafish phenotypic screen, Nat. Chem. Biol., 2011, 8(2), 144-146, DOI: 10.1038/ nchembio.732.
  • 102. E. Lounkine, M. J. Keiser, S. Whitebread, D. Mikhailov, J. Hamon, J.

L. Jenkins, P. Lavan, E. Weber, A. K. Doak, S. Cote, B. K. Shoichet and

L. Urban, Large-scale prediction and testing of drug activity on side-effect targets, Nature, 2012, 486(7403), 361-367, DOI: 10.1038/nature11159.

  • 103. G. Mugumbate, K. A. Abrahams, J. A. G. Cox, G. Papadatos, G. van Westen, J. Lelievre, S. T. Calus, N. J. Loman, L. Ballell, D. Barros, J. P. Overington and G. S. Besra, Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation, PLoS One, 2015, 10(3), e0121492, DOI: 10.1371/journal.pone.0121492.
  • 104., accessed 01.06.2016.
  • 105. Y.-C. Lo, S. Senese, C.-M. Li, Q. Hu, Y. Huang, R. Damoiseaux and J. Z. Torres, Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens, PLoS Comput. Biol., 2015, 11(3), e1004153, DOI: 10.1371/journal.pcbi.1004153.
  • 106., accessed 01.06.2016.
  • 107. N. Huang, B. K. Shoichet and J. J. Irwin, Benchmarking sets for molecular docking, J. Med. Chem., 2006, 49(23), 6789-6801, DOI: 10.1021/ jm0608356.
  • 108. M. M. Mysinger, M. Carchia, J. J. Irwin and B. K. Shoichet, Directory of Useful Decoys, Enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem., 2012, 55(14), 6582-6594, DOI: 10.1021/ jm300687e.
  • 109. N. Brown, In Silico Medicinal Chemistry: Computational Methods to Support Drug Design, RSC, 2016, DOI: 10.1039/9781782622604, ISBN: 978-1-78262-163-8.
  • 110. N. J. Perualila-Tan, Z. Shkedy, W. Talloen, H. W. Gohlmann, M. V. Moer- beke and A. Kasim, Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery, J. Bioinf. Comput. Biol., 2016, 14(4), 1650018, DOI: 10.1142/S0219720016500189.
  • 111. D. G. Sprous, Fingerprint-based clustering applied to define a QSAR model use radius, J. Mol. Graphics Modell., 2008, 27(2), 225-232, DOI: 10.1016/j.jmgm.2008.04.009.
  • 112. M. Zwierzyna, M. Vogt, G. M. Maggiora and J. Bajorath, Design and characterization of chemical space networks for different compound data sets, J. Comput.-AidedMol. Des., 2015, 29(2), 113-125, DOI: 10.1007/ s10822-014-9821-4.
  • 113. B. Zhang, M. Vogt, G. M. Maggiora and J. Bajorath, Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity, J. Comput.-Aided Mol. Des., 2015, 29(7), 595-608, DOI: 10.1007/s10822-015-9852-5.
  • 114. B. Zhang, M. Vogt, G. M. Maggiora and J. Bajorath, Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures, J. Comput.-Aided Mol. Des., 2015, 29(10), 937-950, DOI: 10.1007/s10822-015-9872-1.
  • 115. M. Wu, M. Vogt, G. M. Maggiora and J. Bajorath, Design of chemical space networks on the basis of Tversky similarity, J. Comput.-Aided Mol. Des., 2016, 30(1), 1-12, DOI: 10.1007/s10822-015-9891-y.
  • 116. K. Roy, S. Kar and R. N. Das, A Primer on QSAR/QSPR Modeling, Springer, 2015, DOI: 10.1007/978-3-319-17281-1, ISBN: 978-3-319-17280-4.
  • 117. K. Roy, S. Kar and R. N. Das, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Elsevier, 2015, ISBN: 978-0-12-801505-6.
  • 118. A. Cherkasov, E. N. Muratov, D. Fourches, A. Varnek, i. i. Baskin, M. Cronin, J. Dearden, p. Gramatica, Y. C. Martin, г. Todeschini, V. Consonni,

V. E. Kuz’min, г. Cramer, г. Benigni, C. yang, J. Rathman, L. Terfloth, J. Gasteiger, A. Richard and A. Tropsha, QSAR modeling: Where have you been? Where are you going to? J. Med. Chem., 2014, 57(12), 4977-5010, DOI: 10.1021/jm4004285.

  • 119. T. Wang, M. B. Wu, J. P. Lin and L. R. Yang, Quantitative structure- activity relationship: promising advances in drug discovery platforms, Expert Opin. Drug Discovery, 2015, 10(12), 1283-1300, DOI: 10.1517/17460441.2015.1083006.
  • 120. E. V. Radchenko, D. S. Karlov, V. A. Palyulin, N. S. Zefirov and V. M. Pent- kovski, Computer-aided modeling of activity and selectivity of quinazoli- nones as noncompetitive NMDA receptor antagonists, Dokl. Biochem. Biophys., 2012, 443(1), 118-122, DOI: 10.1134/S1607672912020159.
  • 121. E. V. Radchenko, D. S. Karlov, A. N. Zefirov, V. A. Palyulin, N. S. Zefirov and V. M. Pentkovski, Computer-aided design of negative allosteric modulators of NMDA receptor, Dokl. Biochem. Biophys., 2013, 448(1), 22-26, DOI: 10.1134/S1607672913010079.
  • 122. K. H. Kim and Y. C. Martin, Direct prediction of dissociation constants (pKa's) of clonidine-like imidazolines, 2-substituted imidazoles and 1-methyl-2-substituted-imidazoles from 3D structures using a comparative molecular field analysis (CoMFA) approach, J. Med. Chem., 1991, 34(7), 2056-2060, DOI: 10.1021/jm00111a020.
  • 123. T. Kaserer, K. R. Beck, M. Akram, A. Odermatt and D. Schuster, Pharmacophore models and pharmacophore-based virtual screening: Concepts and applications exemplified on hydroxysteroid dehydrogenases, Molecules, 2015, 20(12), 22799-22832, DOI: 10.3390/ molecules201219880.
  • 124. O. F. Guner and J. P. Bowen, Setting the record straight: The origin of the pharmacophore concept, J. Chem. Inf. Model., 2014, 54(5), 1269-1283, DOI: 10.1021/ci5000533.
  • 125. J. Kazius, R. McGuire and R. Bursi, Derivation and validation of toxico- phores for mutagenicity prediction,J. Med. Chem., 2005, 48(1), 312-320, DOI: 10.1021/jm040835a.
  • 126. C. G. Wermuth, C. R. Ganellin, P. Lindberg and L. A. Mitscher, Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998), Pure Appl. Chem., 1998, 70(5), 1129-1143, DOI: 10.1351/ pac199870051129.
  • 127. G. Wolber, T. Seidel, F. Bendix and T. Langer, Molecule-pharmacophore superpositioning and pattern matching in computational drug design, Drug Discovery Today, 2008, 13(1-2), 23-29, DOI: 10.1016/j. drudis.2007.09.007.
  • 128. S. Y. Yang, Pharmacophore modeling and applications in drug discovery: challenges and recent advances, Drug Discovery Today, 2010, 15(11-12), 444-450, DOI: 10.1016/j.drudis.2010.03.013.
  • 129. T. S. Rush, J. A. Grant, L. Mosyak and A. Nicholls, A shape-based 3-D scaffold hopping method and its application to a bacterial protein- protein interaction,/. Med. Chem., 2005, 48(5), 1489-1495, DOI: 10.1021/ jm040163o.
  • 130. W. H. Shin, X. Zhu, M. G. Bures and D. Kihara, Three-dimensional compound comparison methods and their application in drug discovery, Molecules, 2015,20(7), 12841-12862, DOI: 10.3390/molecules200712841.
  • 131. E. V. Radchenko, D. S. Karlov, V. A. Palyulin and N. S. Zefirov, Molecular modeling of the transmembrane domain of mGluR2 metabotropic glutamate receptor and the binding site of its positive allosteric modulators, Dokl. Biochem. Biophys., 2014, 454(1), 13-16, DOI: 10.1134/ S1607672914010050.
  • 132. vROCS 3.1.2, OpenEye Scientific Software, 2011, http://www.eyesopen. com.
  • 133. M. Wieder, U. Perricone, S. Boresch, T. Seidel and T. Langer, Evaluating the stability of pharmacophore features using molecular dynamics simulations, Biochem. Biophys. Res. Commun., 2016, 470(3), 685-689, DOI: 10.1016/j.bbrc.2016.01.081.
  • 134. A. A. Mel’nikov, V. A. Palyulin, E. V. Radchenko and N. S. Zefirov, Generation of chemical structures on the basis of QSAR models of molecular field topology analysis, Dokl. Chem., 2007, 415(2), 196-199, DOI: 10.1134/S0012500807080058.
  • 135. A. Sun, A. Prussia, W. Zhan, E. E. Murray, J. Doyle, L.-T. Cheng, J.-J. Yoon, E. V. Radchenko, V. A. Palyulin, R. W. Compans, D. C. Liotta, R. K. Plemper and J. P. Snyder, Nonpeptide inhibitors of measles virus entry, J. Med. Chem., 2006, 49(17), 5080-5092, DOI: 10.1021/jm0602559.
  • 136. V. I. Chupakhin, S. V. Bobrov, E. V. Radchenko, V. A. Palyulin and

N. S. Zefirov, Computer-aided design of selective ligands of the benzodiazepine-binding site of the GABAa receptor, Dokl. Chem., 2008, 422(1), 227-230, DOI: 10.1134/S0012500808090073.

  • 137. E. V. Radchenko, G. F. Makhaeva, V. V. Malygin, V. B. Sokolov, V. A. Palyulin and N. S. Zefirov, Modeling of the relationships between the structure of O-phosphorylated oximes and their anticholinesterase activity and selectivity using Molecular Field Topology Analysis (MFTA), Dokl. Biochem. Biophys., 2008, 418(1), 47-51, DOI: 10.1134/S1607672908010122.
  • 138. E. V. Radchenko, G. F. Makhaeva, V. B. Sokolov, V. A. Palyulin and

N. S. Zefirov, Study of the structural determinants of acute and delayed neurotoxicity of O-phosphorylated oximes by Molecular Field Topology Analysis (MFTA), Dokl. Biochem. Biophys., 2009, 429(1), 309-314, DOI: 10.1134/S1607672909060064.

139. E. V. Radchenko, S. O. Koshelev, D. A. Tsareva, A. E. Voronkov, V. A. Pal- yulin and N. S. Zefirov, Computer-aided design of arylphthalazines as potential Smoothened receptor antagonists, Dokl. Chem., 2012, 443(2), 97-100, DOI: 10.1134/S0012500812040027.

  • 140. G. F. Makhaeva, E. V. Radchenko, I. I. Baskin, V. A. Palyulin, R. J. Richardson and N. S. Zefirov, Combined QSAR studies of inhibitor properties of
  • 0- phosphorylated oximes toward serine esterases involved in neurotoxicity, drug metabolism and Alzheimer's disease, SAR QSAR Environ. Res., 2012, 23(7-8), 627-647, DOI: 10.1080/1062936X.2012.679690.
  • 141. E. V. Radchenko, A. A. Mel'nikov, G. F. Makhaeva, V. A. Palyulin and N. S. Zefirov, Molecular design of O-phosphorylated oximes - selective inhibitors of butyrylcholinesterase, Dokl. Biochem. Biophys., 2012, 443(1), 91-95, DOI: 10.1134/S1607672912020093.
  • 142. G. F. Makhaeva, E. V. Radchenko, V. A. Palyulin, E. V. Rudakova, A. Y. Aksinenko, V. B. Sokolov, N. S. Zefirov and R. J. Richardson, Organo- phosphorus compound esterase profiles as predictors of therapeutic and toxic effects, Chem.-Biol. Interact., 2013, 203(1), 231-237, DOI: 10.1016/j.cbi.2012.10.012.
  • 143. E. V. Radchenko, G. F. Makhaeva, N. P. Boltneva, O. G. Serebryakova, I. V. Serkov, A. N. Proshin, V. A. Palyulin and N. S. Zefirov, Molecular design of N,N-disubstituted 2-aminothiazolines as selective inhibitors of car- boxylesterase, Russ. Chem. Bull., 2016, 65(2), 570-575, DOI: 10.1007/ s11172-016-1339-6.
  • 144. A. S. Girgis, S. R. Tala, P. V. Oliferenko, A. A. Oliferenko and A. R. Katritzky, Computer-assisted rational design, synthesis, and bioassay of non-steroidal anti-inflammatory agents, Eur. J. Med. Chem., 2012, 50,
  • 1- 8, DOI: 10.1016/j.ejmech.2011.11.034.
  • 145. P. V. Oliferenko, A. A. Oliferenko, G. I. Poda, D. I. Osolodkin, G. G. Pillai, U. R. Bernier, M. Tsikolia, N. M. Agramonte, G. G. Clark, K. J. Linthicum and A. R. Katritzky, Promising Aedes aegypti repellent chemotypes identified through integrated QSAR, virtual screening, synthesis, and bioassay, PLoS One, 2013, 8(9), e64547, DOI: 10.1371/journal.pone.0064547.
  • 146. P. V. Oliferenko, A. A. Oliferenko, A. S. Girgis, D. O. Saleh, A. M. Srour,

R. F. George, G. G. Pillai, C. S. Panda, C. D. Hall and A. R. Katritzky, Synthesis, bioassay, and Molecular Field Topology Analysis of diverse vasodilatory heterocycles, J. Chem. Inf. Model., 2014, 54(4), 1103-1116, DOI: 10.1021/ci400723m.

  • 147. F. Jabeen, P. V. Oliferenko, A. A. Oliferenko, G. G. Pillai, F. L. Ansari, C. D. Hall and A. R. Katritzky, Dual inhibition of the a-glucosidase and buty- rylcholinesterase studied by Molecular Field Topology Analysis, Eur. J. Med. Chem., 2014, 80, 228-242, DOI: 10.1016/j.ejmech.2014.04.018.
  • 148. J. Verma, V. M. Khedkar and E. C. Coutinho, 3D-QSAR in drug design - a review, Curr. Top. Med. Chem., 2010, 10(1), 95-115, DOI: 10.2174/156802610790232260.
  • 149. Sybyl 8.0, Sybyl-X2.1, Certara L.P., 2008-2013,
  • 150. R. D. Cramer, Topomer CoMFA: A design methodology for rapid lead optimization, J. Med. Chem., 2003, 46(3), 374-388, DOI: 10.1021/jm020194o.
  • 151. R. D. Cramer, Rethinking 3D-QSAR, J. Comput.-Aided Mol. Des., 2011, 25(3), 197-201, DOI: 10.1007/s10822-010-9403-z.
  • 152. R. D. Cramer and B. Wendt, Template CoMFA: The 3D-QSAR grail? J. Chem. Inf. Model., 2014, 54(2), 660-671, DOI: 10.1021/ci400696v.
  • 153. B. Wendt and R. D. Cramer, Challenging the gold standard for 3D-QSAR: template CoMFA versus X-ray alignment, J. Comput.-Aided Mol. Des., 2014, 28(8), 803-824, DOI: 10.1007/s10822-014-9761-z.
  • 154. R. D. Cramer, Template CoMFA applied to 116 biological targets, J. Chem. Inf. Model., 2014, 54(7), 2147-2156, DOI: 10.1021/ci500230a.
  • 155. R. D. Cramer, Template CoMFA generates single 3D-QSAR models that, for twelve of twelve biological targets, predict all ChEMBL-tabulated affinities, PLoS One, 2015, 10(6), e0129307, DOI: 10.1371/journal.pone.0129307.
  • 156. P. Tosco, T. Balle and F. Shiri, Open3DALIGN: an open-source software aimed at unsupervised ligand alignment, J. Comput.-Aided Mol. Des., 2011, 25(8), 777-783, DOI: 10.1007/s10822-011-9462-9.
  • 157. P. Tosco and T. Balle, Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields, J. Mol. Model., 2011, 17(1), 201-208, DOI: 10.1007/s00894-010-0684-x.
  • 158., accessed 01.06.2016.
  • 159. S. B. Sosnin, E. V. Radchenko, V. A. Palyulin and N. S. Zefirov, Generalized fragmental approach in QSAR/QSPR studies, Dokl. Chem., 2015, 463(1), 185-188, DOI: 10.1134/S0012500815070071.
  • 160. A. S. Dyabina, E. V. Radchenko, V. A. Palyulin and N. S. Zefirov, Prediction of blood-brain barrier permeability of organic compounds, Dokl. Bio- chem. Biophys., 2016, 470(1), 371-374, DOI: 10.1134/S1607672916050173.
  • 161. S. J. Lusher and T. Ritschel, Finding the right approach to big data- driven medicinal chemistry, Future Med. Chem., 2015, 7(10), 1213-1216, DOI: 10.4155/fmc.15.58.
  • 162. I. V. Tetko, O. Engkvist, U. Koch, J. L. Reymond and H. Chen, BIGCHEM: Challenges and opportunities for Big Data analysis in chemistry, Mol. Inf., 2016, 35(11-12), 615-621, DOI: 10.1002/minf.201600073.
  • 163. M. K. Gilson, T. Liu, M. Baitaluk, G. Nicola, L. Hwang and J. Chong, BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology, Nucleic Acids Res., 2016, 44(D1), D1045-D1053, DOI: 10.1093/nar/gkv1072.
  • 164. S. Kim, P. A. Thiessen, E. E. Bolton, J. Chen, G. Fu, A. Gindulyte, L. Han, J. He, S. He, B. A. Shoemaker, J. Wang, B. Yu, J. Zhang and S. H. Bryant, PubChem Substance and Compound databases, Nucleic Acids Res., 2016, 44(D1), D1202-D1213, DOI: 10.1093/nar/gkv951.
  • 165. Y. Wang, T. Suzek, J. Zhang, J. Wang, S. He, T. Cheng, B. A. Shoemaker,

A. Gindulyte and S. H. Bryant, PubChem BioAssay: 2014 update, Nucleic Acids Res., 2014, 42(D1), D1075-D1082, DOI: 10.1093/nar/gkt978.

  • 166. T. I. Oprea, M. Olah, L. Ostopovici, R. Rad and M. Mracec, On the propagation of errors in the QSAR literature, in EuroQSAR 2002 - Designing Drugs and Crop Protectants: Processes, Problems and Solutions, ed. M. Ford, D. Livingstone, J. Dearden and H. van de Waterbeemd, Blackwell, 2003, pp. 314-315.
  • 167. D. Fourches, E. Muratov and A. Tropsha, Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research, J. Chem. Inf. Model., 2010, 50(7), 1189-1204, DOI: 10.1021/ci100176x.
  • 168. A. J. Williams and S. Ekins, A quality alert and call for improved curation of public chemistry databases, Drug Discovery Today, 2011, 16(17-18), 747-750, DOI: 10.1016/j.drudis.2011.07.007.
  • 169. B. Zdrazil, M. Pinto, P. Vasanthanathan, A. J. Williams, L. Z. Balderud,

O. Engkvist, C. Chichester, A. Hersey, J. P. Overington and G. F. Ecker, Annotating human P-glycoprotein bioassay data, Mol. Inf., 2012, 31(8), 599-609, DOI: 10.1002/minf.201200059.

  • 170. D. A. Tsareva and G. F. Ecker, How far could we go with open data - A case study for TRPV1 antagonists, Mol. Inf., 2013, 32(5-6), 555-562, DOI: 10.1002/minf.201300019.
  • 171. O. A. Tarasova, A. F. Urusova, D. A. Filimonov, M. C. Nicklaus, A. V. Zakharov and V. V. Poroikov, QSAR modeling using large-scale databases: Case study for HIV-1 reverse transcriptase inhibitors, J. Chem. Inf. Model., 2015, 55(7), 1388-1399, DOI: 10.1021/acs.jcim.5b00019.
  • 172. A. A. Nikitina, A. A. Orlov, D. I. Osolodkin, V. A. Palyulin and N. S. Zefirov, Analysis and visualization of antiviral chemical space, 21st European Symposium on Quantitative Structure-Activity Relationship (EuroQSAR 2016), Verona, Italy, September 4-8, 2016, p. P180.
  • 173. E. Ahlberg, A. Amberg, L. D. Beilke, D. Bower, K. P. Cross, L. Custer,

K. A. Ford, J. Van Gompel, J. Harvey, M. Honma, R. Jolly, E. Joossens,

R. A. Kemper, M. Kenyon, N. Kruhlak, L. Kuhnke, P. Leavitt, R. Naven,

C. Neilan, D. P. Quigley, D. Shuey, H.-P. Spirkl, L. Stavitskaya, A. Teas- dale, A. White, J. Wichard, C. Zwickl and G. J. Myatt, Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity, Regul. Toxicol. Pharmacol., 2016, 77, 1-12, DOI: 10.1016/j.yrtph.2016.02.003.

174. L. Tao, P. Zhang, C. Qin, S. Y. Chen, C. Zhang, Z. Chen, F. Zhu,

S. Y. Yang, Y. Q. Wei and Y. Z. Chen, Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools, Adv. Drug Delivery Rev., 2015, 86, 83-100, DOI: 10.1016/j.addr. 2015.03.014.

  • 175. V. G. Maltarollo, J. C. Gertrudes, P. R. Oliveira and K. M. Honorio, Applying machine learning techniques for ADME-Tox prediction: a review, Expert Opin. Drug Metab. Toxicol., 2015, 11(2), 259-271, DOI: 10.1517/17425255.2015.980814.
  • 176. A. B. Raies and V. B. Bajic, In silico toxicology: computational methods for the prediction of chemical toxicity, Wiley Interdiscip. Rev.: Comput. Mol. Sci., 2016, 6(2), 147-172, DOI: 10.1002/wcms.1240.
  • 177. G. F. Mangiatordi, D. Alberga, C. D. Altomare, A. Carotti, M. Catto, S. Cellamare, D. Gadaleta, G. Lattanzi, F. Leonetti, L. Pisani, A. Stefanachi, D. Trisciuzzi and O. Nicolotti, Mind the gap! A journey towards computational toxicology, Mol. Inf., 2016, 35(8-9), 294-308, DOI: 10.1002/minf.201501017.
  • 178. F. Sanz, P. Carrio, O. Lopez, L. Capoferri, D. P. Kooi, N. P. Vermeulen,

D. P. Geerke, F. Montanari, G. F. Ecker, C. H. Schwab, T. Kleinoder, T. Magdziarz and M. Pastor, Integrative modeling strategies for predicting drug toxicities at the eTOX project, Mol. Inf., 2015, 34(6-7), 477-484, DOI: 10.1002/minf.201400193.

  • 179. M. Nendza, S. Gabbert, R. Kuhne, A. Lombardo, A. Roncaglioni, E. Ben- fenati, г. Benigni, C. Bossa, S. Strempel, M. Scheringer, a. Fernandez, R. Rallo, F. Giralt, S. Dimitrov, O. Mekenyan, F. Bringezu and G. Schuur- mann, A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH, Regul. Toxicol. Pharmacol., 2013, 66(3), 301-314, DOI: 10.1016/j.yrtph.2013.05.007.
  • 180. A. Sutter, A. Amberg, S. Boyer, A. Brigo, J. F. Contrera, L. L. Custer, K. L. Dobo, V. Gervais, S. Glowienke, J. van Gompel, N. Greene, W. Muster, J. Nicolette, M. V. Reddy, V. Thybaud, E. Vock, A. T. White and L. Muller, Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities, Regul. Toxicol. Pharmacol., 2013, 67(1), 39-52, DOI: 10.1016/j.yrtph.2013.05.001.
  • 181. A. G. Dossetter, E. J. Griffen and A. G. Leach, Matched Molecular Pair Analysis in drug discovery, Drug Discovery Today, 2013, 18(15-16), 724731, DOI: 10.1016/j.drudis.2013.03.003.
  • 182. C. Kramer, J. E. Fuchs, S. Whitebread, P. Gedeck and K. R. Liedl, Matched Molecular Pair Analysis: Significance and the impact of experimental uncertainty, J. Med. Chem., 2014, 57(9), 3786-3802, DOI: 10.1021/ jm500317a.
  • 183. Y. Sushko, S. Novotarskyi, R. Korner, J. Vogt, A. Abdelaziz and I. V. Tetko, Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process, J. Cheminf., 2014, 6, 48, DOI: 10.1186/s13321-014-0048-0.
  • 184. J. M. Beck and C. Springer, Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses, J. Chem. Inf. Model., 2014, 54(4), 1226-1234, DOI: 10.1021/ci500012n.
  • 185. A. de la Vega de Leon and J. Bajorath, Prediction of compound potency changes in matched molecular pairs using support vector regression, J. Chem. Inf. Model., 2014, 54(10), 2654-2663, DOI: 10.1021/ci5003944.
  • 186. D. J. Warner, M. H. Bridgland-Taylor, C. E. Sefton and D. J. Wood, Prospective prediction of antitarget activity by Matched Molecular Pairs Analysis, Mol. Inf., 2012, 31(5), 365-368, DOI: 10.1002/minf.201200020.
  • 187. A. de la Vega de Leon, Y. Hu and J. Bajorath, Systematic identification of matching molecular series and mapping of screening hits, Mol. Inf., 2014, 33(4), 257-263, DOI: 10.1002/minf.201400017.
  • 188. Predictive ADMET: Integrated Approaches in Drug Discovery and Development, ed. J. Wang and L. Urban, Wiley, 2014, ISBN: 978-1-118-29992-0.
  • 189. J. Wang and T. Hou, Advances in computationally modeling human oral bioavailability, Adv. Drug Delivery Rev., 2015, 86, 11-16, DOI: 10.1016/j. addr.2015.01.001.
  • 190. F. T. Silva and G. H. Trossini, The survey of the use of QSAR methods to determine intestinal absorption and oral bioavailability during drug design, Med. Chem., 2014, 10(5), 441-448, DOI: 10.2174/1573406410666 140415122115.
  • 191. E. V. Radchenko, A. S. Dyabina, V. A. Palyulin and N. S. Zefirov, Prediction of human intestinal absorption of drug compounds, Russ. Chem. Bull., 2016, 65(2), 576-580, DOI: 10.1007/s11172-016-1340-0.
  • 192. G. Lambrinidis, T. Vallianatou and A. Tsantili-Kakoulidou, In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review, Adv. Drug Delivery Rev., 2015, 86, 27-45, DOI: 10.1016/j. addr.2015.03.011.
  • 193. K. Lanevskij, P. Japertas and R. Didziapetris, Improving the prediction of drug disposition in the brain, Expert Opin. DrugMetab. Toxicol., 2013, 9(4), 473-486, DOI: 10.1517/17425255.2013.754423.
  • 194. L. Olsen, C. Oostenbrink and F. S. Jorgensen, Prediction of cytochrome P450 mediated metabolism, Adv. Drug Delivery Rev., 2015, 86, 61-71, DOI: 10.1016/j.addr.2015.04.020.
  • 195. B. Feng, J. L. LaPerle, G. Chang and M. V. Varma, Renal clearance in drug discovery and development: molecular descriptors, drug transporters and disease state, Expert Opin. Drug Metab. Toxicol., 2010, 6(8), 939-952, DOI: 10.1517/17425255.2010.482930.
  • 196. J. G. Hengstler, H. Foth, R. Kahl, P. J. Kramer, W. Lilienblum, T. Schulz and H. Schweinfurth, The REACH concept and its impact on toxicological sciences, Toxicology, 2006, 220(2-3), 232-239, DOI: 10.1016/j. tox.2005.12.005.
  • 197. C. M. Auer, J. V. Nabholz and K. P. Baetcke, Mode of action and the assessment of chemical hazards in the presence of limited data: Use of structure-activity relationships (SAR) under TSCA, Section 5, Environ. Health Perspect, 1990, 87, 183-197, DOI: 10.2307/3431024.
  • 198. Organisation for Economic Co-Operation and Development, OECD Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models, 2004, icalsafety/risk-assessment/37849783.pdf, accessed 01.06.2016.
  • 199. European Chemicals Agency, Guidance for the implementation of REACH. Guidance on Information Requirements and Chemical Safety Assessment. Chapter R.6: QSARs and Grouping of Chemicals, May 2008, https://echa. en.pdf, accessed 01.06.2016.
  • 200. J. Devillers and H. Devillers, Prediction of acute mammalian toxicity from QSARs and interspecies correlations, SAR QSAR Environ. Res., 2009, 20(5-6), 467-500, DOI: 10.1080/10629360903278651.
  • 201. A. Lagunin, A. Zakharov, D. Filimonov and V. Poroikov, QSAR modelling of rat acute toxicity on the basis of PASS prediction, Mol. Inf., 2011, 30(2-3), 241-250, DOI: 10.1002/minf.201000151.
  • 202. M. Cassotti, D. Ballabio, V. Consonni, A. Mauri, I. V. Tetko and R. Todeschini, Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method, Altern. Lab. Anim., 2014, 42(1), 31-41. toward-daphnia-magna-by-using-the-ga-knn-method/, accessed 01.06. 2016.
  • 203. P. Gramatica, S. Cassania and A. Sangiona, Aquatic ecotoxicity of personal care products: QSAR models and ranking for prioritization and safer alternatives’ design, Green Chem., 2016, 18(16), 4393-4406, DOI: 10.1039/C5GC02818C.
  • 204. R. Benigni and C. Bossa, Mechanisms of chemical carcinogenicity and mutagenicity: A review with implications for predictive toxicology, Chem. Rev., 2011, 111(4), 2507-2536, DOI: 10.1021/cr100222q.
  • 205. R. Benigni, C. Bossa, O. Tcheremenskaia and A. Giuliani, Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays, Expert Opin. DrugMetab. Toxicol., 2010, 6(7), 809-819, DOI: 10.1517/17425255.2010.486400.
  • 206. К. H. Hsu, B. H. Su, Y. S. Tu, O. A. Lin and Y. J. Tseng, Mutagenicity in a molecule: Identification of core structural features of mutagenicity using a scaffold analysis, PLoS One, 2016, 11(2), e0148900, DOI: 10.1371/journal.pone.0148900.
  • 207. B. O. Villoutreix and O. Taboureau, Computational investigations of hERG channel blockers: New insights and current predictive models, Adv. Drug Delivery Rev., 2015, 86, 72-82, DOI: 10.1016/j.addr.2015.03.003.
  • 208. E. V. Radchenko, Yu. A. Rulev, A. Ya. Safanyaev, V. A. Palyulin and N. S. Zefirov, Computer-aided estimation of the hERG-mediated cardiotoxicity risk of potential drug compounds, Dokl. Biochem. Biophys., 2017, 473(1), in press.
  • 209. M. Chen, H. Bisgin, L. Tong, H. Hong, H. Fang, J. Borlak and W. Tong, Toward predictive models for drug-induced liver injury in humans: are we there yet?, Biomarkers Med., 2014, 8(2), 201-213, DOI: 10.2217/ bmm.13.146.
  • 210. M. Novic and M. Vracko, QSAR models for reproductive toxicity and endocrine disruption activity, Molecules, 2010, 15(3), 1987-1999, DOI: 10.3390/molecules15031987.
  • 211. A. Rybacka, C. Ruden, I. V. Tetko and P. L. Andersson, Identifying potential endocrine disruptors among industrial chemicals and their metabolites - development and evaluation of in silico tools, Chemosphere,
  • 2015, 139, 372-378, DOI: 10.1016/j.chemosphere.2015.07.036.
  • 212. R. Huang, M. Xia, S. Sakamuru, J. Zhao, S. A. Shahane, M. Attene- Ramos, T. Zhao, C. P. Austin and A. Simeonov, Modelling the Tox21 10 К chemical profiles for in vivo toxicity prediction and mechanism characterization, Nat. Commun., 2016, 7, 10425, DOI: 10.1038/ncomms10425.
  • 213. R. Huang, M. Xia, D.-T. Nguyen, T. Zhao, S. Sakamuru, J. Zhao, S. A. Shahane, A. Rossoshek and A. Simeonov, Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs, Front. Environ. Sci.,
  • 2016, 3, 85, DOI: 10.3389/fenvs.2015.00085.
  • 214. M. N. Drwal, V. B. Siramshetty, P. Banerjee, A. Goede, R. Preissner and M. Dunkel, Molecular similarity-based predictions of the Tox21 screening outcome, Front. Environ. Sci., 2015, 3, 54, DOI: 10.3389/ fenvs.2015.00054.
  • 215. F. Stefaniak, Prediction of compounds activity in nuclear receptor signaling and stress pathway assays using machine learning algorithms and low-dimensional molecular descriptors, Front. Environ. Sci., 2015, 3, 77, DOI: 10.3389/fenvs.2015.00077.
  • 216. A. Mayr, G. Klambauer, T. Unterthiner and S. Hochreiter, DeepTox: Toxicity prediction using deep learning, Front. Environ. Sci., 2016, 3, 80, doI: 10.3389/fenvs.2015.00080.
  • 217. a. Abdelaziz, H. Spahn-Langguth, k.-W. Schramm and I. V. Tetko, Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 Challenge, Front. Environ. Sci., 2016, 4, 2, Doi: 10.3389/fenvs.2016.00002.
  • 218. S. J. Capuzzi, R. politi, o. Isayev, S. Farag and A. Tropsha, QSAR modeling of Tox21 Challenge stress response and nuclear receptor signaling toxicity assays, Front. Environ. Sci., 2016, 4, 3, Doi: 10.3389/ fenvs.2016.00003.
  • 219. Y. uesawa, rigorous selection of random forest models for identifying compounds that activate toxicity-related pathways, Front. Environ. Sci., 2016, 4, 9, Doi: 10.3389/fenvs.2016.00009.
  • 220. A. Koutsoukas, J. St. Amand, M. Mishra and J. Huan, predictive toxicology: Modeling chemical induced toxicological response combining circular fingerprints with random forest and support vector machine, Front. Environ. Sci., 2016, 4, 11, Doi: 10.3389/fenvs.2016.00011.
  • 221. к. Ribay, M. T. Kim, W. Wang, D. pinolini and H. Zhu, predictive modeling of estrogen receptor binding agents using advanced cheminformat- ics tools and massive public data, Front. Environ. Sci., 2016, 4, 12, Doi: 10.3389/fenvs.2016.00012.
  • 222. G. Barta, identifying biological pathway interrupting toxins using multi-tree ensembles, Front. Environ. Sci., 2016, 4, 52, Doi: 10.3389/ fenvs.2016.00052.
  • 223. S. M. Ivanov, A. A. Lagunin and V. V. poroikov, In silico assessment of adverse drug reactions and associated mechanisms, Drug Discovery Today, 2016, 21(1), 58-71, Doi: 10.1016/j.drudis.2015.07.018.
  • 224. N. Ai, X. Fan and S. Ekins, In silico methods for predicting drug-drug interactions with cytochrome p-450s, transporters and beyond, Adv. Drug Delivery Rev., 2015, 86, 46-60, Doi: 10.1016/j.addr.2015.03.006.
  • 225. F. Montanari and G. F. Ecker, prediction of drug-ABC-transporter interaction - Recent advances and future challenges, Adv. Drug Delivery Rev., 2015, 86, 17-26, Doi: 10.1016/j.addr.2015.03.001.
  • 226. F. Klepsch, p. Vasanthanathan and G. F. Ecker, Ligand and structure-based classification models for prediction of p-glycoprotein inhibi- tors,J. Chem. Inf. Model., 2014, 54(1), 218-229, Doi: 10.1021/ci400289j.
  • 227. A. V. Zakharov, E. V. Varlamova, A. A. Lagunin, A. V. Dmitriev, E. N. Muratov, D. Fourches, V. E. Kuz’min, V. V. poroikov, A. Tropsha and

M. C. Nicklaus, QSAR modeling and prediction of drug-drug interactions, Mol. Pharmaceutics, 2016, 13(2), 545-556, Doi: 10.1021/acs. molpharmaceut.5b00762.

228. S. Ekins, A. J. Williams, M. D. Krasowski and J. S. Freundlich, In silico repositioning of approved drugs for rare and neglected diseases, Drug Discovery Today, 2011, 16(7-8), 298-310, Doi: 10.1016/j. drudis.2011.02.016.

  • 229. F. Schmidt, H. Matter, G. Hessler and A. Czich, Predictive in silico off- target profiling in drug discovery, Future Med. Chem., 2014, 6(3), 295-317, Doi: 10.4155/fmc.13.202.
  • 230. L. C. Huang, X. Wu and J. Y. Chen, predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures, Proteomics, 2013, 13(2), 313-324, DOI: 10.1002/pmic.201200337.
  • 231. A. V. Stepanchikova, A. A. Lagunin, D. A. Filimonov and V. V. Poroikov, prediction of biological activity spectra for substances: evaluation on the diverse sets of drug-like structures, Curr. Med. Chem., 2003, 10(3), 225-233, DOI: 10.2174/0929867033368510.
  • 232. p. V. pogodin, A. A. Lagunin, D. A. Filimonov and V. V. poroikov, pASS Targets: Ligand-based multi-target computational system based on a public data and naive Bayes approach, SAR QSAR Environ. Res., 2015, 26(10), 783-793, DOI: 10.1080/1062936X.2015.1078407.
  • 233. A. V. Zakharov, A. A. Lagunin, D. A. Filimonov and V. V. Poroikov, Quantitative prediction of antitarget interaction profiles for chemical compounds, Chem. Res. Toxicol., 2012, 25(11), 2378-2385, DOI: 10.1021/ tx300247r.
  • 234. D. Filimonov, V. Poroikov, Y. Borodina and T. Gloriozova, Chemical similarity assessment through Multilevel Neighborhoods of Atoms: Definition and comparison with the other descriptors, J. Chem. Inf. Comput. Sci., 1999, 39(4), 666-670, DOI: 10.1021/ci980335o.
  • 235. D. A. Filimonov, A. V. Zakharov, A. A. Lagunin and V. V. Poroikov, QNA- based 'Star Track' QSAR approach, SAR QSAR Environ. Res., 2009, 20(7-8), 679-709, DOI: 10.1080/10629360903438370.
  • 236. A. Lagunin, D. Filimonov and V. Poroikov, Multi-targeted natural products evaluation based on biological activity prediction with PASS, Curr. Pharm. Des., 2010, 16(15), 1703-1717, DOI: 10.2174/ 138161210791164063.
  • 237. G. V. Kokurkina, M. D. Dutov, S. A. Shevelev, S. V. Popkov, A. V. Zakharov and V. V. Poroikov, Synthesis, antifungal activity and QSAR study of
  • 2-arylhydroxynitroindoles, Eur. J. Med. Chem., 2011, 46(9), 4374-4382, DOI: 10.1016/j.ejmech.2011.07.008.
  • 238. S. A. Kryzhanovskii, R. M. Salimov, A. A. Lagunin, D. A. Filimonov,

T. A. Gloriozova and V. V. Poroikov, Nootropic action of some antihypertensive drugs: computer predicting and experimental testing, Pharm. Chem. J., 2012, 45(10), 605-611, DOI: 10.1007/s11094-012-0689-0.

  • 239. M. N. Kurilo, F. V. Ryzhkov, P. V. Karpov, E. V. Radchenko, V. A. Palyulin and N. S. Zefirov, Molecular design of selective ligands of chemokine receptors, Dokl. Biochem. Biophys., 2015, 461(1), 131-134, DOI: 10.1134/ S1607672915020167.
  • 240. R. Mannhold, G. I. Poda, C. Ostermann and I. V. Tetko, Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96 000 compounds, J. Pharm. Sci., 2009, 98(3), 861893, DOI: 10.1002/jps.21494.
  • 241. I. V. Tetko, G. I. Poda, C. Ostermann and R. Mannhold, Large-scale evaluation of log p predictors: local corrections may compensate insufficient accuracy and need of experimentally testing every other compound, Chem. Biodiversity, 2009, 6(11), 1837-1844, DOI: 10.1002/ cbdv.200900075.
  • 242. I. V. Tetko, S. Novotarskyi, I. Sushko, V. Ivanov, A. E. Petrenko, R. Die- den, F. Lebon and B. Mathieu, Development of dimethyl sulfoxide solubility models using 163 000 molecules: Using a domain applicability metric to select more reliable predictions, J. Chem. Inf. Model., 2013, 53(8), 1990-2000, DOI: 10.1021/ci400213d.
  • 243. I. V. Tetko, Y. Sushko, S. Novotarskyi, L. Patiny, I. Kondratov, A. E. Petrenko, L. Charochkina and A. M. Asiri, How accurately can we predict the melting points of drug-like compounds? J. Chem. Inf. Model., 2014, 54(12), 3320-3329, DOI: 10.1021/ci5005288.
  • 244. I. V. Tetko, D. M. Lowe and A. J. Williams, The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS, J. Cheminf., 2016, 8, 2, DOI: 10.1186/s13321-016-0113-y.
  • 245. S. Vorberg and I. V. Tetko, Modeling the biodegradability of chemical compounds using the Online CHEmical Modeling Environment (OCHEM), Mol. Inf., 2014, 33(1), 73-85, DOI: 10.1002/minf.201300030.
  • 246., accessed 01.06.2016.
  • 247. A. V. Rudik, A. V. Dmitriev, A. A. Lagunin, D. A. Filimonov and V. V. Poroi- kov, Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm, J. Chem. Inf. Model., 2014, 54(2), 498-507, DOI: 10.1021/ci400472j.
  • 248., accessed 01.06.2016.
  • 249. I. Sushko, S. Novotarskyi, R. Korner, A. K. Pandey, M. Rupp, W. Teetz, S. Brandmaier, A. Abdelaziz, V. V. Prokopenko, V. Y. Tanchuk, R. Todeschini, A. Varnek, G. Marcou, P. Ertl, V. Potemkin, M. Grishina, J. Gasteiger, C. Schwab, I. I. Baskin, V. A. Palyulin, E. V. Radchenko, W. J. Welsh, V. Kholodovych, D. Chekmarev, A. Cherkasov, J. Aires-de-Sousa,

Q. Y. Zhang, A. Bender, F. Nigsch, L. Patiny, A. Williams, V. Tkachenko and I. V. Tetko, Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information, J. Comput.-Aided Mol. Des., 2011, 25(6), 533-554, DOI: 10.1007/s10822-011-9440-2.

  • 250. I. Sushko, E. Salmina, V. A. Potemkin, G. Poda and I. V. Tetko, ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions,J. Chem. Inf. Model., 2012, 52(8), 23102316, DOI: 10.1021/ci300245q.
  • 251., accessed 01.06.2016.
< Prev   CONTENTS   Source   Next >