On the interpretation and interpretability of quantitative structure-activity relationship models

Journal of Computer-Aided Molecular Design

Volumn 22, Issue 12, 2008, Pages 857-871

  Guha, Rajarshi ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

Interpretation; Linear regression; Neural network; Partial least squares (PLS); Quantitative structure activity relationship (QSAR)

Indexed keywords

BIOACTIVITY; COMPUTATIONAL CHEMISTRY; ENCODING (SYMBOLS); LEAST SQUARES APPROXIMATIONS; LINEAR REGRESSION; MOLECULAR GRAPHICS; STRUCTURES (BUILT OBJECTS);

ENCODINGS; INTERPRETABILITY; INTERPRETATION; NEURAL-NETWORKS; PARTIAL LEAST SQUARE; PARTIAL LEAST-SQUARES; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODELING; STRUCTURE-ACTIVITY RELATIONSHIPS;

NEURAL NETWORKS;

2 AZIRIDINYL 1,4 NAPHTHOQUINONYL DERIVATIVE; 2,3 BIS(AZIRIDINYL) 1,4 NAPHTHOQUINONYL DERIVATIVE; 2,4 DIAMINO 5 (BENZYL)PYRIMIDINE DERIVATIVE; AMPRENAVIR; ANTIMALARIAL AGENT; ARTEMISININ DERIVATIVE; BENZAMIDINE DERIVATIVE; CYTOCHROME P450 2D6; CYTOCHROME P450 3A4; DIHYDROFOLATE REDUCTASE INHIBITOR; INTEGRASE INHIBITOR; POTASSIUM CHANNEL BLOCKING AGENT; PROTEINASE INHIBITOR; SULFONAMIDE; THIOCARBAMIC ACID DERIVATIVE; UNCLASSIFIED DRUG;

ACCURACY; ANTIMALARIAL ACTIVITY; ANTIVIRAL ACTIVITY; ARTICLE; DRUG ABSORPTION; DRUG DESIGN; DRUG DISTRIBUTION; DRUG EXCRETION; DRUG METABOLISM; DRUG STRUCTURE; DRUG SYNTHESIS; DRUG TARGETING; ENZYME INHIBITION; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS 1; IC 50; MATHEMATICAL MODEL; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; STATISTICAL MODEL; STRUCTURE ACTIVITY RELATION;

EID: 56049095031     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-008-9240-5     Document Type: Article

References (117)

1. Agrafiotis DK, Cedeño W (2002) Feature selection for structure-activity correlation using binary particle swarms. J Med Chem 45:1098-1107
2. Agrawal V, Sharma R, Khadikar P (2002) QSAR studies on antimalarial substituted phenyl analogues and their n ω oxides. Bioorg Med Chem 10(5):1361-1366
3. Arakawa M, Hasegawa K, Funatsu K (2006) QSAR study of anti-HIV HEPT analogues based on multiobjective genetic programming and counter-propagation neural network. Chemom Intel Lab Syst 83:91-98
4. Banks J (1985) Nomograms. In: Encyclopedia of statistical sciences, vol 6. Wiley, New York
5. Bender A, Mussa H, Glen R, Reiling S (2004) Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance. J Chem Inf Comput Sci 44(5):1708-1718
6. Besalu E, Gallegos A, Carbo-Dorca R (2001) Topological quantum similarity indices and their use in QSAR: Application to several families of antimalarial compounds. Commun Math Comp Chem 44:41-64
7. Breiman L (2001) Statistical modeling: Two cultures. Stat Sci 16:199-231
8. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman & Hall/CRC, Boca Raton, FL
9. Bremser W (1978) HOSE - a novel substructure code. Anal Chim Acta 103:355-365
10. Brown N, McKay B, Gasteiger J (2006) A novel workflow for the inverse QSPR problem using multiobjective optimization. J Comput Aided Mol Des 20:333-341
11. Burden F (1989) Molecular identification number for substructure searches. J Chem Inf Comput Sci 29:225-227
12. Byvatov E, Baringhaus KH, Schneider G, Matter H (2007) A virtual screening filter for identification of cytochrome P450 2C9 (CYP2C9) inhibitors. QSAR Comb Sci 26:618-628
13. Carbo-Dorca R, Leyda L, Arnau M (1980) How similar is a molecule to another? An electron density measure of similarity between two molecular structures. Int J Quantum Chem 17(6):1185-1189
14. Chastrette M, Zakarya D, Peyraud J (1994) Structure-musk odor relationships for tetralins and indans using neural networks (on the contribution of descriptors to the classification). Eur J Med Chem 29:343-348
15. Chatterjee S, Hadi A (1986) Influential observations, high leverage points, and outliers in linear regression. Stat Sci 1(3):379-416
16. Chin T, So S (2004) Development of neural network QSPR models for Hansch substituent constants. 2. Applications in QSAR studies of HIV-1 reverse transcriptase and dihydrofolate reductase inhibitors. J Chem Inf Comput Sci 44:154-160
17. Cho BH, Yu H, Lee J, Chee YJ, Kim IY, Kim SI (2008) Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels. IEEE Trans Inf Technol Biomed 12:247-256
18. Chohan K, Paine S, Mistry J, Barton P, Davis A (2005) A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. J Med Chem 48:5154-5161
19. Colmenarejo G, Pedraglio A, Lavandera J (2001) Cheminformatic models to predict binding affinities to human serum albumin. J Med Chem 44(25):4370-4378
20. Consonni V, Todeschini R, Pavan M, Gramatica P (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. Part 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci 42(3):693-705
21. Cruz-Monteagudo M, Borges F, Perez Gonzalez M, Dias Soeiro Cordeiro MN (2007) Computational modeling tools for the design of potent antimalarial bisbenzamidines: Overcoming the antimalarial potential of pentamidine. Bioorg Med Chem 15:5322-5339
22. De Lucca G, Liang J, De Lucca I (1999) Stereospecific synthesis, structure-activity relationship, and oral bioavailability of tetrahydropyrimidin-2-one HIV protease inhibitors. J Med Chem 42(1):135-152
23. Dias Selassie C, Li Rl, Poe M, Hansch C (1991) Optimization of hydrophobic and hydrophilic substituent interactions of 2,4-diamino-5-(substituted-benzyl)pyrimidines with dihydrofolate reductase. J Med Chem 34(1):46-54
24. Dietrich SW, Blaney JM, Reynolds MA, Jow PYC, Hansch C (1980) Quantitative structure-selectivity relationships. Comparison of the inhibition of Escherichia coli and bovine liver dihydrofolate reductase by 5-(substituted benzyl)-2,4-diaminopyrimidines. J Med Chem 23(11):1205-1212
25. Diller DJ, Hobbs DW (2007) Understanding hERG inhibition with QSAR models based on a one dimensional molecular representation. J Comput Aided Mol Des 21:379-393
26. Diudea M (1997) Cluj matrix invariants. J Chem Inf Comput Sci 37:300-305
27. Doweyko A (2008) QSAR: Dead or alive? J Comput Aided Mol Des 22:81-89
28. Durant J, Leland B, Henry D, Nourse J (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42(6):1273-1280
29. Dutta D, Guha R, Chen T, Wild D (2007) Ensemble feature selection: consistent descriptor subsets for multiple QSAR models. J Chem Inf Model 47(3):989-997
30. Eriksson L, Johansson E, Lindgren F, Sjostrom M, Wold S (2002) Megavariate analysis of hierarchical QSAR data. J Comput Aided Mol Des 16:711-726
31. Estrada E (2002) Physicochemical interpretation of molecular connectivity indices. J Phys Chem A 106:9085-9091
32. Fernandez M, Caballero J (2006) Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks. Bioorg Med Chem 14:280-294
33. Franke L, Schwarz O, Muller-Kuhrt L, Hoernig C, Fischer L, George S, Tanrikulu Y, Schneider P, Werz O, Steinhilber D, Schneider G (2007) Identification of natural-product-derived inhibitors of 5-lipoxygenase activity by ligand-based virtual screening. J Med Chem 50(11):2640-2646
34. Gangjee A, Yu J, McGuire J, Cody V, Galitsky N, Kisliuk R, Queener S (2000) Design, synthesis, and X-ray crystal structure of a potent dual inhibitor of thymidylate synthase and dihydrofolate reductase as an antitumor agent. J Med Chem 43:3837-3851
35. Garcia-Domenech R, Galvez J, de Julian-Ortiz J, Pogliani L (2008) Some new trends in chemical graph theory. Chem Rev 108(3):1127-1169
36. Garg R, Bhhatarai B (2004) A mechanistic study of 3-aminoindazole cyclic urea HIV-1 protease inhibitors using comparative QSAR. Bioorg Med Chem 12(22):5819-5831
37. Garson D (1991) Interpreting neural network connection strengths. AI Expert 6(7):47-51
38. Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer S (2007) Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J Comput Aided Mol Des 21:189-206
39. Girones X, Gallegos A, Carbo-Dorca R (2001) Antimalarial activity of synthetic 1,2,4-trioxanes and cyclic peroxy ketals, a quantum similarity study. J Comput Aided Mol Des 15:1053-1063
40. Gleeson MP, Davis AM, Chohan KK, Paine SW, Boyer S, Gavaghan CL, Arnby CH, Kankkonen C, Albertson N (2007) Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models. J Comput Aided Mol Des 21:559-573
41. Gramatica P (2007) Principles of QSAR models validation: Internal and external. QSAR Comb Sci 26(5):694-701
42. Guha R, Jurs P (2004) Development of linear, ensemble, and nonlinear models for the prediction and interpretation of the biological activity of a set of PDGFR inhibitors. J Chem Inf Comput Sci 44(6):2179-2189
43. Guha R, Jurs P (2004) The development of QSAR models to predict and interpret the biological activity of artemisinin analogues. J Chem Inf Comput Sci 44:1440-1449
44. Guha R, Jurs P (2005) Interpreting computational neural network QSAR models: A measure of descriptor importance. J Chem Inf Model 45:800-806
45. Guha R, Schürer S (2008) Utilizing high throughput screening data for predictive toxicology models: Protocols and application to MLSCN assays. J Comput Aided Mol Des 22(6-7):367-384
46. Guha R, Stanton D, Jurs P (2005) Interpreting computational neural network QSAR models: A detailed interpretation of the weights and biases. J Chem Inf Model 45:1109-1121
47. Gunturi S, Narayanan R, Khandelwal A (2006) In silico ADME modelling 2: computational models to predict human serum albumin binding affinity using ant colony systems. Bioorg Med Chem 14:4118-4129
48. Hansch C (1969) A quantitative approach to biochemical structure-activity relationships. Acc Chem Res 2:232-239
49. Hansch C, Fujita T (1964) ε-σ-φ analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616-1626
50. Hansch C, Leo A, Hoekman D (1995) Exploring QSAR: Hydrophobic, electronic, and steric constants. American Chemical Society, Washington, DC
51. Hassan M, Brown RD, Varma-O'Brien S, Rogers D (2006) Cheminformatics analysis and learning in a data pipelining environment. Mol Divers 10(3):283-299
52. Hemmer M, Gasteiger J (2000) Prediction of three-dimensional molecular structures using information from infrared spectra. Anal Chim Acta 420:145-154
53. Hemmer M, Steinhauer V, Gasteiger J (1999) Deriving the 3D structure of organic molecules from their infrared spectra. Vib Spectrosc 19:151-164
54. Hirst J (1996) Nonlinear quantitative structure-activity relationship for the inhibition of dihydrofolate reductase by pyrimidines. J Med Chem 39(18):3526-3532
55. Ivanciuc O, Braun W (2007) Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors. Protein Pept Lett 14:903-916
56. Johnson S (2008) The trouble with QSAR (or how I learned to stop worrying and embrace fallacy). J Chem Inf Model 48(1):25-26
57. Katritzky A, Petrukhin R, Tatham D, Basak S, Benfenati E, Karelson M, Maran U (2001) Interpretation of quantitative structure-property and -activity relationships. J Chem Inf Comput Sci 41:679-685
58. Katritzky A, Oliferenko A, Lomaka A, Karelson M (2002) Six-membered cyclic ureas as HIV-1 protease inhibitors: A QSAR study based on CODESSA PRO approach. Bioorg Med Chem Lett 12:3453-3457
59. Katritzky A, Kulshyn O, Stoyanova-Slavova I, Dobehev D, Kuanar M, Fara D, Karelson M (2006) Antimalarial activity: A QSAR modeling using CODESSA PRO software. Bioorg Med Chem 14:2333-2357
60. Kier L, Hall L (1986) Molecular connectivity in structure-activity analysis. Wiley, New York
61. Kier L, Hall L (1999) Molecular structure description: The electrotopological state. Academic Press, Burlington, MA
62. Kiralj R, Ferreira M (2003) A priori molecular descriptors in QSAR: A case of HIV-1 protease inhibitors. I. The chemometric approach. J Mol Graph Model 21:435-448
63. Klayman D (1985) Qinghaosu (artemisinin): An antimalarial drug from China. Science 228:1049
64. Klon A, Heroux A, Ross L, Pathak V, Johnson C, Piper J, Borhani D (2002) Atomic structures of human dihydrofolate reductase complexed with NADPH and two lipophilic antifolates at 1.09 Angstrom and 1.05 Angstrom resolution. J Mol Biol 320:677-693
65. Kramer C, Beck B, Kriegl JM, Clark T (2008) A composite model for hERG blockade. ChemMedChem 3:254-265
66. Leonard JT, Roy K (2007) Comparative classical QSAR modeling of anti-HIV thiocarbamates. QSAR Comb Sci 26:980-990
67. Lewis RA (2005) A general method for exploiting QSAR models in lead optimization. J Med Chem 48:1638-1648
68. Lin TS, Zhu LY, Xu SP, Divo AA, Sartorelli AC (1991) Synthesis and antimalarial activity of 2-aziridinyl- and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives. J Med Chem 34(5):1634-1639
69. Mackay A (1977) Scientific quotations: Harvest of a quiet eye. Crane, Russak & Co, New York
70. Masek B, Shen L, Smith K, Pearlman R (2008) Sharing chemical information without sharing chemical structure. J Chem Inf Model 48(2):256-261
71. Miller A (2002) Subset selection in regression, 2nd edn. Chapman & Hall/CRC, Boca Raton, FL
72. Miller JF, Brieger M, Furfine ES, Hazen RJ, Kaldor I, Reynolds D, Sherrill RG, Spaltenstein A (2005) Novel P1 chain-extended HIV protease inhibitors possessing potent anti-HIV activity and remarkable inverse antiviral resistance profiles. Bioorg Med Chem Lett 15(15):3496-3500
73. Moreau G, Broto P (1980) Autocorrelation of molecular structures: application to SAR studies. Nouv J Chim 4:757-764
74. Navia-Vázquez A, Parrado-Hernández E (2006) Support vector machine interpretation. Neurocomputing 69:1754-1759
75. Ney H (1995) On the probabilistic interpretation of neural network classifiers and discriminative training criteria. IEEE Trans Pattern Anal Mach Intel 17:107-119
76. Nguyen-Cong V, Van Dang G, Rode B (1996) Using multivariate adaptive regression splines to QSAR studies of dihydroartemisinin derivatives. Eur J Med Chem 31:797-803
77. Otzen T, Wempe E, Kunz B, Bartels R, Lehwark-Yvetot G, Hansel W, Schaper K, Seydel J (2004) Folate-synthesizing enzyme system as target for development of inhibitors and inhibitor combinations against candida albicans-synthesis and biological activity of new 2,4-diaminopyrimidines and 4′-substituted 4-aminodiphenyl sulfones. J Med Chem 47:240-253
78. Pearlman RS, Smith KM (1999) Metric validation and the receptor-relevant subspace concept. J Chem Inf Comput Sci 39:28-35
79. Pinheiro J, Kiralj R, Ferreira M, Romero O (2003) Artemisinin derivatives with antimalarial activity against Plasmodium Falciparum designed with the aid of quantum chemical and partial least squares methods. QSAR Comb Sci 22:830-842
80. Polanski J, Zouhiri F, Jeanson L, Desmaele D, D'Angelo J, Mouscadet J, Gieleciak R, Gasteiger J, Le Bret M (2002) Use of the Kohonen neural network for rapid screening of ex vivo anti-HIV activity of styrylquinolines. J Med Chem 45:4647-4654
81. Purdy R (1996) A mechanism-mediated model for carcinogenicity: Model content and prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 25 organic chemicals. Environ Health Perspect 104:1085-1094
82. Randic M (1978) Fragment search in acyclic structures. J Chem Inf Comput Sci 18(2):101-107
83. Randic M, Zupan J (2001) On interpretation of well-known topological indices. J Chem Inf Comput Sci 41: 550-560
84. Randic M, Balaban A, Basak S (2001) On structural interpretation of several distance related topological indices. J Chem Inf Comput Sci 41:593-601
85. Ravichandran V, Jain PK, Mourya VK, Agrawal RK (2007) QSAR study on some arylsulfonamides as anti-HIV agents. Med Chem Res 16:342-351
86. Renner S, Fechner U, Schneider G (2006) Pharmacophores and pharmacophore searches, methods and principles in medicinal chemistry, vol 32, chap Alignment-free pharmacophore patterns-a correlation vector approach. Wiley-VCH, Weinheim, Germany, pp 49-79
87. Roche O, Schneider P, Zuegge J, Guba W, Kansy M, Alanine A, Bleicher K, Danel F, Gutknecht EM, Rogers-Evans M, Neidhart W, Stalder H, Dillon M, Sjogren E, Fotouhi N, Gillespie P, Goodnow R, Harris W, Jones P, Taniguchi M, Tsujii S, von der Saal W, Zimmermann G, Schneider G (2002) Development of a virtual screening method for identification of "frequent hitters" in compound libraries. J Med Chem 45(1):137-142
88. Rubner J, Schulten K, Tavan P (1990) A self organizing network for complete feature selection. In: International conference on parallel processing in neural systems and computers. Elsevier, Dusseldorf
89. Sahu KK, Ravichandran V, Mourya VK, Agrawal K (2007) QSAR analysis of caffeoyl naphthalene sulfonamide derivatives as HIV-1 integrase inhibitors. Med Chem Res 15:418-430
90. Sato M, Tsukimoto H (2001) Rule extraction from neural networks via decision tree induction. In: Neural networks, proceedings international Joint conference, vol 3. IEEE Computer Society, Los Alamitos, CA, USA, pp 1870-1875
91. Schölkopf B, Smola A (2002) Learning with Kernels. MIT Press, Cambridge, MA
92. Sheridan RP, Korzekwa KR, Torres RA, Walker MJ (2007) Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. J Med Chem 50:3173-3184
93. Stanton D (2003) On the physical interpretation of QSAR models. J Chem Inf Comput Sci 43(5):1423-1433
94. Stanton D, Jurs P (1990) Development and use of charged partial surface area structural descriptors in computer assisted quantitative structure property relationship studies. Anal Chem 62:2323-2329
95. Stanton D, Mattioni BE, Knittel J, Jurs P (2004) Development and use of hydrophobic surface area (HSA) descriptors for computer assisted quantitative structure-activity and structure-property relationship studies. J Chem Inf Comput Sci 44:1010-1023
96. Summerfield R, Daigle D, Mayer S, Mallik D, Hughes D, Jackson S, Sulek M, Organ M, Brown E, Junop M (2006) A 2.13A structure of E. coli dihydrofolate reductase bound to a novel competitive inhibitor reveals a new binding surface involving the M20 loop region. J Med Chem 49(24):6977-6986
97. Sutter J, Dixon S, Jurs P (1995) Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J Chem Inf Comput Sci 35:77-84
98. Taha I, Ghosh J (1999) Symbolic interpretation of artificial neural networks. IEEE Trans Knowl Data Eng 11:448-463
99. Takahashi T (1991) An information theoretical interpretation of neuronal activities. In: Neural networks, proceedings International Joint Conference, vol 2. IEEE Computer Society, Los Alamitos, CA, USA, pp 645-648
100. Tang LJ, Zhou YP, Jiang JH, Zou HY, Wu HL, Shen GL, Yu RQ (2007) Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies. J Chem Inf Model 47:1438-1445
101. Tian F, Zhou P, Lv F, Song R, Li Z (2007) Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): A novel rotation-translation invariant 3D structure descriptor and its applications to peptides. J Pept Sci 13:549-566
102. Todeschini R, Consonni V (2002) Handbook of molecular descriptors. Wiley-VCH, Berlin
103. Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69-77
104. Urbanek S (2006) Graphics of large datasets, chap Trees, pp. 177-2002. Statistics and Computing. Springer, Berlin Heidelberg
105. Urbanek S, Unwin A (2002) Making trees interactive with KLIMT - a COSADA software project. Stat Comp Graph Newsl 13(1):13-16
106. Usdun B, Melssen WJ, Buydens LMC (2007) Visualisation and interpretation of support vector regression models. Anal Chim Acta 595:299-309
107. Vapnik V (1998) Statistical learning theory. Wiley, New York
108. Venkatraman V, Dalby AR, Yang ZR (2004) Evaluation of mutual information and genetic programming for feature selection in QSAR. J Chem Inf Comput Sci 44:1686-1692
109. Ventura C, Martins F (2008) Application of quantitative structure-activity relationships to the modeling of antitubercular compounds. 1. The hydrazide family. J Med Chem 51(3):612-624
110. Verma RP, Hansch C, Selassie CD (2007) Comparative QSAR studies on PAMPA/ modified PAMPA for high throughput profiling of drug absorption potential with respect to Caco-2 cells and human intestinal absorption. J Comput Aided Mol Des 21:3-22
111. 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 49:1118-1124
112. Visco D, Pophale R, Rintoul M, Faulon J (2002) Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor. J Mol Graph Model 20:429-438
113. Weininger D, Weininger A, Weininger J (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29:97-101
114. Yuan H, Parrill A (2002) QSAR studies of HIV-1 integrase inhibition. Bioorg Med Chem 10(12):4169-4183
115. Zahouily M, Lazar M, Elmakssoudi A, Rakik J, Elaychi S, Rayadh A (2006) QSAR for anti-malarial activity of 2-aziridinyl and 2,3-bis(aziridinyl)-1,4-naphthoquinonyl sulfonate and acylate derivatives. J Mol Model 12:398-405
116. Zhang H, Li H, Ma Q (2007) QSAR study of a large set of 3-pyridyl ethers as ligands of the a-4 b -2 nicotinic acetylcholine receptor. J Mol Graph Model 26:226-235
117. Zhou D, Alelyunas Y, Liu R (2008) Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility. J Chem Inf Model 48(5):981-987