Structural and Physico-Chemical Interpretation (SPCI) of QSAR Models and Its Comparison with Matched Molecular Pair Analysis

Journal of Chemical Information and Modeling

Volumn 56, Issue 8, 2016, Pages 1455-1469

  Polishchuk, Pavel ^a,b   Tinkov, Oleg ^c   Khristova, Tatiana ^b,d   Ognichenko, Ludmila ^b   Kosinskaya, Anna ^b   Varnek, Alexandre ^d,e   Kuz'Min, Victor ^b  

^a PALACKÝ UNIVERSITY   (Czech Republic)

^b A V BOGATSKY PHYSICO CHEMICAL INSTITUTE   (Ukraine)

^c T G Shevchenko Transdniestria State University   (Moldova)

^d UNIVERSITÉ DE STRASBOURG   (France)

^e KAZAN FEDERAL UNIVERSITY   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ANALYSIS; COMPUTATIONAL CHEMISTRY; GRAPHICAL USER INTERFACES; MOLECULAR GRAPHICS; PHYSICOCHEMICAL PROPERTIES;

BLOOD-BRAIN BARRIER; DIVERSE DATA SETS; MACHINE LEARNING METHODS; PHYSICO-CHEMICAL FACTORS; PHYSICO-CHEMICALS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS; RECEPTOR ANTAGONISTS; STRUCTURE ACTIVITY;

LEARNING SYSTEMS;

ARGINYL-GLYCYL-ASPARTIC ACID; FIBRINOGEN RECEPTOR; OLIGOPEPTIDE; PEPTIDOMIMETIC AGENT;

ANIMAL; ANTAGONISTS AND INHIBITORS; BIOLOGY; BLOOD BRAIN BARRIER; CHEMICAL PHENOMENA; CHEMISTRY; COMPARATIVE STUDY; COMPUTER INTERFACE; DATA MINING; DRUG DESIGN; METABOLISM; ORAL DRUG ADMINISTRATION; PERMEABILITY; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RAT; SOFTWARE; TOXICITY TESTING;

ADMINISTRATION, ORAL; ANIMALS; BLOOD-BRAIN BARRIER; CHEMICAL PHENOMENA; COMPUTATIONAL BIOLOGY; DATA MINING; DRUG DESIGN; OLIGOPEPTIDES; PEPTIDOMIMETICS; PERMEABILITY; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RATS; RECEPTORS, FIBRINOGEN; SOFTWARE; TOXICITY TESTS; USER-COMPUTER INTERFACE;

EID: 84983651830     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.6b00371     Document Type: Article

References (71)

1. Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin, I. I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.; Todeschini, R. et al. QSAR Modeling: Where Have You Been? Where Are You Going To? J. Med. Chem. 2014, 57, 4977-5010 10.1021/jm4004285
2. Gasteiger, J. Chemoinformatics: Achievements and Challenges, a Personal View Molecules 2016, 21, 151 10.3390/molecules21020151
3. Kuz'min, V. E.; Polishchuk, P. G.; Artemenko, A. G.; Andronati, S. A. Interpretation of QSAR models based on Random Forest method Mol. Inf. 2011, 30, 593-603 10.1002/minf.201000173
4. Carlsson, L.; Helgee, E. A.; Boyer, S. Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data J. Chem. Inf. Model. 2009, 49, 2551-2558 10.1021/ci9002206
5. Rosenbaum, L.; Hinselmann, G.; Jahn, A.; Zell, A. Interpreting linear support vector machine models with heat map molecule coloring J. Cheminf. 2011, 3, 11 10.1186/1758-2946-3-11
6. Baskin, I. I.; Ait, A. O.; Halberstam, N. M.; Palyulin, V. A.; Zefirov, N. S. An approach to the interpretation of backpropagation neural network models in QSAR studies SAR QSAR Environ. Res. 2002, 13, 35-41 10.1080/10629360290002073
7. Guha, R.; Jurs, P. C. Interpreting Computational Neural Network QSAR Models: A Measure of Descriptor Importance J. Chem. Inf. Model. 2005, 45, 800-806 10.1021/ci050022a
8. Chen, H.; Carlsson, L.; Eriksson, M.; Varkonyi, P.; Norinder, U.; Nilsson, I. Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms J. Chem. Inf. Model. 2013, 53, 1324-1336 10.1021/ci4001376
9. Marcou, G.; Horvath, D.; Solov'ev, V.; Arrault, A.; Vayer, P.; Varnek, A. Interpretability of SAR/QSAR Models of any Complexity by Atomic Contributions Mol. Inf. 2012, 31, 639-642 10.1002/minf.201100136
10. Stålring, J.; Almeida, P. R.; Carlsson, L.; Helgee Ahlberg, E.; Hasselgren, C.; Boyer, S. Localized Heuristic Inverse Quantitative Structure Activity Relationship with Bulk Descriptors Using Numerical Gradients J. Chem. Inf. Model. 2013, 53, 2001-2017 10.1021/ci400281y
11. Kuz'min, V. E.; Artemenko, A. G.; Polischuk, P. G.; Muratov, E. N.; Hromov, A. I.; Liahovskiy, A. V.; Andronati, S. A.; Makan, S. Y. Hierarchic System of QSAR Models (1D-4D) on the Base of Simplex Representation of Molecular Structure J. Mol. Model. 2005, 11, 457-467 10.1007/s00894-005-0237-x
12. Hasegawa, K.; Migita, K.; Funatsu, K. Atom Coloring for Chemical Interpretation and De Novo Design for Molecular Design. In Knowledge-Oriented Applications in Data Mining; Funatsu, K., Ed.; InTech: Rijeka, Croatia, 2011.
13. An, Y.; Sherman, W.; Dixon, S. L. Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization J. Chem. Inf. Model. 2013, 53, 2312-2321 10.1021/ci400250c
14. Polishchuk, P. G.; Kuz'min, V. E.; Artemenko, A. G.; Muratov, E. N. Universal Approach for Structural Interpretation of QSAR/QSPR Models Mol. Inf. 2013, 32, 843-853 10.1002/minf.201300029
15. Webb, S.; Hanser, T.; Howlin, B.; Krause, P.; Vessey, J. Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity J. Cheminf. 2014, 6, 8 10.1186/1758-2946-6-8
16. Sushko, Y.; Novotarskyi, S.; Korner, R.; Vogt, J.; Abdelaziz, A.; Tetko, I. Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process J. Cheminf. 2014, 6, 48 10.1186/s13321-014-0048-0
17. Riniker, S.; Landrum, G. Similarity maps-a visualization strategy for molecular fingerprints and machine-learning methods J. Cheminf. 2013, 5, 43 10.1186/1758-2946-5-43
18. Leach, A. G.; Jones, H. D.; Cosgrove, D. A.; Kenny, P. W.; Ruston, L.; MacFaul, P.; Wood, J. M.; Colclough, N.; Law, B. Matched Molecular Pairs as a Guide in the Optimization of Pharmaceutical Properties; a Study of Aqueous Solubility, Plasma Protein Binding and Oral Exposure J. Med. Chem. 2006, 49, 6672-6682 10.1021/jm0605233
19. Wassermann, A. M.; Haebel, P.; Weskamp, N.; Bajorath, J. SAR Matrices: Automated Extraction of Information-Rich SAR Tables from Large Compound Data Sets J. Chem. Inf. Model. 2012, 52, 1769-1776 10.1021/ci300206e
20. Kramer, C.; Fuchs, J. E.; Whitebread, S.; Gedeck, P.; Liedl, K. R. Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty J. Med. Chem. 2014, 57, 3786-3802 10.1021/jm500317a
21. O'Boyle, N. M.; Boström, J.; Sayle, R. A.; Gill, A. Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity J. Med. Chem. 2014, 57, 2704-2713 10.1021/jm500022q
22. Free, S. M.; Wilson, J. W. A Mathematical Contribution to Structure-Activity Studies J. Med. Chem. 1964, 7, 395-399 10.1021/jm00334a001
23. Polishchuk, P. G.; Samoylenko, G. V.; Khristova, T. M.; Krysko, O. L.; Kabanova, T. A.; Kabanov, V. M.; Kornylov, A. Y.; Klimchuk, O.; Langer, T.; Andronati, S. A. et al. Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents J. Med. Chem. 2015, 58, 7681-7694 10.1021/acs.jmedchem.5b00865
24. Garg, P.; Verma, J. In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model J. Chem. Inf. Model. 2006, 46, 289-297 10.1021/ci050303i
25. Wichmann, K.; Diedenhofen, M.; Klamt, A. Prediction of Blood-Brain Partitioning and Human Serum Albumin Binding Based on COSMO-RS σ-Moments J. Chem. Inf. Model. 2007, 47, 228-233 10.1021/ci600385w
26. Gerebtzoff, G.; Seelig, A. In Silico Prediction of Blood-Brain Barrier Permeation Using the Calculated Molecular Cross-Sectional Area as Main Parameter J. Chem. Inf. Model. 2006, 46, 2638-2650 10.1021/ci0600814
27. Deconinck, E.; Zhang, M. H.; Coomans, D.; Vander Heyden, Y. Classification Tree Models for the Prediction of Blood-Brain Barrier Passage of Drugs J. Chem. Inf. Model. 2006, 46, 1410-1419 10.1021/ci050518s
28. Li, H.; Yap, C. W.; Ung, C. Y.; Xue, Y.; Cao, Z. W.; Chen, Y. Z. Effect of Selection of Molecular Descriptors on the Prediction of Blood-Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods J. Chem. Inf. Model. 2005, 45, 1376-1384 10.1021/ci050135u
29. Fu, X. C.; Song, Z. F.; Fu, C. Y.; Liang, W. Q. A simple predictive model for blood-brain barrier penetration Pharmazie 2005, 60, 354-358
30. Zhang, L.; Zhu, H.; Oprea, T.; Golbraikh, A.; Tropsha, A. QSAR Modeling of the Blood-Brain Barrier Permeability for Diverse Organic Compounds Pharm. Res. 2008, 25, 1902-1914 10.1007/s11095-008-9609-0
31. Katritzky, A. R.; Kuanar, M.; Slavov, S.; Dobchev, D. A.; Fara, D. C.; Karelson, M.; Acree, W. E., Jr; Solov'ev, V. P.; Varnek, A. Correlation of blood-brain penetration using structural descriptors Bioorg. Med. Chem. 2006, 14, 4888-4917 10.1016/j.bmc.2006.03.012
32. Muehlbacher, M.; Spitzer, G.; Liedl, K.; Kornhuber, J. Qualitative prediction of blood-brain barrier permeability on a large and refined dataset J. Comput.-Aided Mol. Des. 2011, 25, 1095-1106 10.1007/s10822-011-9478-1
33. Gartner, T. K.; Bennett, J. S. The tetrapeptide analogue of the cell attachment site of fibronectin inhibits platelet aggregation and fibrinogen binding to activated platelets J. Biol. Chem. 1985, 260, 11891-11894
34. Andrieux, A.; Hudry-Clergeon, G.; Ryckewaert, J. J.; Chapel, A.; Ginsberg, M. H.; Plow, E. F.; Marguerie, G. Amino acid sequences in fibrinogen mediating its interaction with its platelet receptor, GPIIbIIIa J. Biol. Chem. 1989, 264, 9258-9265
35. Scarborough, R. M.; Naughton, M. A.; Teng, W.; Rose, J. W.; Phillips, D. R.; Nannizzi, L.; Arfsten, A.; Campbell, A. M.; Charo, I. F. Design of potent and specific integrin antagonists. Peptide antagonists with high specificity for glycoprotein IIb-IIIa J. Biol. Chem. 1993, 268, 1066-1073
36. Hartman, G. D.; Egbertson, M. S.; Halczenko, W.; Laswell, W. L.; Duggan, M. E.; Smith, R. L.; Naylor, A. M.; Manno, P. D.; Lynch, R. J. Non-peptide fibrinogen receptor antagonists. 1. Discovery and design of exosite inhibitors J. Med. Chem. 1992, 35, 4640-4642 10.1021/jm00102a020
37. Egbertson, M. S.; Chang, C. T. C.; Duggan, M. E.; Gould, R. J.; Halczenko, W.; Hartman, G. D.; Laswell, W. L.; Lynch, J. J.; Lynch, R. J. Non-Peptide Fibrinogen Receptor Antagonists. 2. Optimization of a Tyrosine Template as a Mimic for Arg-Gly-Asp J. Med. Chem. 1994, 37, 2537-2551 10.1021/jm00042a007
38. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B. et al. ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res. 2012, 40, D1100-D1107 10.1093/nar/gkr777
39. Mehrotra, M. M.; Heath, J. A.; Smyth, M. S.; Pandey, A.; Rose, J. W.; Seroogy, J. M.; Volkots, D. L.; Nannizzi-Alaimo, L.; Park, G. L.; Lambing, J. L. et al. Discovery of Novel 2,8-Diazaspiro[4.5]decanes as Orally Active Glycoprotein IIb-IIIa Antagonists† J. Med. Chem. 2004, 47, 2037-2061 10.1021/jm030354b
40. http://www2.epa.gov/chemical-research/toxicity-estimation-software-tool-test.
41. Kuz'min, V. E.; Artemenko, A. G.; Muratov, E. N. Hierarchical QSAR technology based on the Simplex representation of molecular structure J. Comput.-Aided Mol. Des. 2008, 22, 403-421 10.1007/s10822-008-9179-6
42. cxcalc, version 5.4; Chemaxon: Budapest, Hungary.
43. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. et al. Scikit-learn: Machine Learning in Python J. Mach. Learn. Res. 2011, 12, 2825-2830
44. Standardizer, version 5.4; ChemAxon: Budapest, Hungary.
45. Polishchuk, P. G. SiRMS: Simplex Representation of Molecular Structure, version 0.1; 2013-2015.
46. Hussain, J.; Rea, C. Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets J. Chem. Inf. Model. 2010, 50, 339-348 10.1021/ci900450m
47. RDKit: Open-Source Cheminformatics. http://www.rdkit.org.
48. Rankovic, Z. CNS Drug Design: Balancing Physicochemical Properties for Optimal Brain Exposure J. Med. Chem. 2015, 58, 2584-2608 10.1021/jm501535r
49. Ghose, A. K.; Herbertz, T.; Hudkins, R. L.; Dorsey, B. D.; Mallamo, J. P. Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery ACS Chem. Neurosci. 2012, 3, 50-68 10.1021/cn200100h
50. Wager, T. T.; Chandrasekaran, R. Y.; Hou, X.; Troutman, M. D.; Verhoest, P. R.; Villalobos, A.; Will, Y. Defining Desirable Central Nervous System Drug Space through the Alignment of Molecular Properties, in Vitro ADME, and Safety Attributes ACS Chem. Neurosci. 2010, 1, 420-434 10.1021/cn100007x
51. Wager, T. T.; Hou, X.; Verhoest, P. R.; Villalobos, A. Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties ACS Chem. Neurosci. 2010, 1, 435-449 10.1021/cn100008c
52. Hitchcock, S. A.; Pennington, L. D. Structure-Brain Exposure Relationships J. Med. Chem. 2006, 49, 7559-7583 10.1021/jm060642i
53. Hitchcock, S. A. Structural Modifications that Alter the P-Glycoprotein Efflux Properties of Compounds J. Med. Chem. 2012, 55, 4877-4895 10.1021/jm201136z
54. Springer, T. A.; Zhu, J.; Xiao, T. Structural basis for distinctive recognition of fibrinogen γC peptide by the platelet integrin αIIbβ3 J. Cell Biol. 2008, 182, 791-800 10.1083/jcb.200801146
55. Kazius, J.; McGuire, R.; Bursi, R. Derivation and Validation of Toxicophores for Mutagenicity Prediction J. Med. Chem. 2005, 48, 312-320 10.1021/jm040835a
56. Beechey, R. B. The uncoupling of respiratory-chain phosphorylation by 4,5,6,7-tetrachloro-2-trifluoromethylbenzimidazole Biochem. J. 1966, 98, 284-289 10.1042/bj0980284
57. Čolović, M. B.; Krstić, D. Z.; Lazarević-Pašti, T. D.; Bondžić, A. M.; Vasić, V. M. Acetylcholinesterase Inhibitors: Pharmacology and Toxicology Curr. Neuropharmacol. 2013, 11, 315-335 10.2174/1570159X11311030006
58. Littin, K. E.; O'Connor, C. E.; Eason, C. T. Comparative effects of brodifacoum on rats and possums N. Z. Plant Prot. 2000, 53, 310-315
59. Terada, H. Uncouplers of oxidative phosphorylation Environ. Health Perspect. 1990, 87, 213-218 10.1289/ehp.9087213
60. Grundlingh, J.; Dargan, P.; El-Zanfaly, M.; Wood, D. 2,4-Dinitrophenol (DNP): A Weight Loss Agent with Significant Acute Toxicity and Risk of Death J. Med. Toxicol. 2011, 7, 205-212 10.1007/s13181-011-0162-6
61. Casarett, L. J.; Klaassen, C. D. Casarett and Doull's Toxicology: The Basic Science of Poisons, 7 th ed.; McGraw-Hill Medical: New York, 2008; p 1309.
62. Valchev, I.; Binev, R.; Yordanova, V.; Nikolov, Y. Anticoagulant Rodenticide Intoxication in Animals-A Review Turk. J. Vet. Anim. Sci. 2008, 32, 237-243
63. Medina, M. A. The in vivo effects of hydrazines and vitamin B6 on the metabolism of gamma-aminobutyric acid J. Pharmacol. Exp. Ther. 1963, 140, 133-137
64. O'Brien, R. D.; Kirkpatrick, M.; Miller, P. S. Poisoning of the rat by hydrazine and alkylhydrazines Toxicol. Appl. Pharmacol. 1964, 6, 371-377 10.1016/S0041-008X(64)80001-6
65. Proudfoot, A.; Bradberry, S.; Vale, J. A. Sodium Fluoroacetate Poisoning Toxicol. Rev. 2006, 25, 213-219 10.2165/00139709-200625040-00002
66. Davidson, B.; Soodak, M.; Strout, H. V.; Neary, J. T.; Nakamura, C.; Maloof, F. Thiourea and Cyanamide as Inhibitors of Thyroid Peroxidase: The Role of Iodide Endocrinology 1979, 104, 919-924 10.1210/endo-104-4-919
67. Bukowska, B. Toxicity of 2,4-Dichlorophenoxyacetic Acid-Molecular Mechanisms Polym. J. Environ. Stud. 2006, 15, 365-374
68. Thiourea; Concise International Chemical Assessment Document 49; World Health Organization: Geneva, 2003.
69. Polishchuk, P. G. SPCI: Structural and Physico-Chemical Interpretation Tool, version 0.1.3; 2014-2015.
70. Pérez-Garrido, A.; Rivero-Buceta, V.; Cano, G.; Kumar, S.; Pérez-Sánchez, H.; Bautista, M. Latest QSAR study of adenosine A2B receptor affinity of xanthines and deazaxanthines Mol. Diversity 2015, 19, 975-989 10.1007/s11030-015-9608-0
71. Zhang, Y.-Y.; Liu, H.; Summerfield, S. G.; Luscombe, C. N.; Sahi, J. Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System Mol. Pharmaceutics 2016, 13, 1540-1550 10.1021/acs.molpharmaceut.6b00031