Aihara, J., 1976. A generalized total π J. -...
Transcript of Aihara, J., 1976. A generalized total π J. -...
References
277
Aihara, J., 1976. A generalized total π-energy index for a conjugated hydrocarbon. J.
Org. Chem. 41, 2488-2490.
Akamatsu M., 2002. Current state and perspectives of 3D -QSAR; Curr. Top. Med.
Chem. 2, 1381-1394.
Alaeiyan, M., Asadpour, J., 2011. The edge szeged index and the revised Szeged
index of bridge graphs. World Appl. Sci. J. 13, 2344-2349.
American Cancer Society. 2013. Global cancer facts and figures. [online]. Available at:
http://www.cancer.org/acs/groups/content/@epidemiologysurveilance (Accessed
on 21/12/ 2013).
Amic, D., Trinajstic, N., 1995. On the Detour matrix. Croat. Chem. Acta. 68, 53-62.
Andova, V., Petrusevski, M., 2011. Variable Zagreb indices and Karamata’s
inequality. MATCH Commun. Math. Comput. Chem. 65, 685-690.
Andres, C., Hutter, M.C., 2006. CNS permeability of drugs predicted by a decision
tree. QSAR & Comb. Sci. 25, 305-309.
Anyanwu, M.N., Shiva, S.G. 2009. Comparative analysis of serial decision tree
classification algorithms. Int. J. Comput. Sci. Security. 3, 230-240.
Arabie, P., Hubert, L.J., De Soete, G., 1996. Clustering and Classification, World
Scientific Singapore, Singapore.
Ashrafi, A.R., Doslic, T., Hamzeh, A., 2010. The Zagreb co-indices of graph
operations. Discrete Appl. Math. 158, 1571-1578.
Babic, D., Klein, D.J., Lukovits, I., Nikolic, S., Trinajstic, N., 2002. Resistance-
distance matrix: a computational algorithm and its application. Int. J. Quant.
Chem. 90, 166-176.
Bailey, D.S., Brown, D., 2001. High-throughput chemistry and structure-based
design, survival of the smartest. Drug Discovery Today. 6, 57-59.
Bajaj, S., 2005. Study on topochemical descriptors for the prediction of
physiochemical and biological properties of molecules. Ph.D. Thesis, Guru
Gobind Singh Indraprastha University, Delhi, India.
Bajaj, S., Sambi, S.S., Madan, A.K., 2004a. Predicting anti-HIV activity of
phenyltiazolethiourea PETT analogs: computational approach using Wiener’s
topochemical index. J. Mol. Str. THEOCHEM. 684, 197-203.
References
278
Bajaj, S., Sambi, S.S., Madan, A.K., 2004b. Prediction of carbonic anhydrase
activation by tri-/tetrasubstituted-pyridinium-azole drugs: A computational
approach using novel topochemical descriptor. QSAR & Comb. Sci. 23, 506-
514.
Bajaj, S., Sambi, S.S., Madan, A.K., 2005a. Prediction of anti-inflammatory activity
of N-arylanthranilic acids: Computational approach using refined Zagreb
indices. Croat. Chem. Acta. 78, 165-174.
Bajaj, S., Sambi, S.S., Madan, A.K., 2005b. Topochemical models for prediction of
anti-tumor activity of 3-aminopyrazoles. Chem. Pharm. Bull. 53, 611-615.
Bajaj, S., Sambi, S.S., Madan, A.K., 2005c.Topochemical model for prediction of
anti-HIV activity of HEPT analogs. Bioorg. Med. Chem. Lett. 2, 467-469.
Bajaj, S., Sambi, S.S., Madan, A.K., 2005d. Topochemical models for prediction of
anti-HIV activity of 4-benzyl pyridinone derivatives. Drug Dev. Ind. Pharm. 31,
1041-1051.
Bajaj, S., Sambi, S.S., Madan, A.K., 2006. Model for prediction of anti-HIV activity
of 2-pyridinone derivatives using novel topological descriptor. QSAR & Comb.
Sci. 25, 813-823.
Balaban, A. T., Balaban, T.S., 1991. New vertex invariants and topological indices of
chemical graphs based on information on distances. J. Math. Chem. 8, 383-397.
Balaban, A. T., Balaban, T.S., 1992. Correlations using topological indices based on
real graph invariants. J. Chim. Phys. 89, 1735-1745.
Balaban, A.T., 1975. Some applications of graph theory. MATCH Commun. Math.
Comput. Chem. 1, 33-60.
Balaban, A.T., 1976. Chemical Applications of Graph Theory. Academic Press, New
York.
Balaban, A.T., 1979. Chemical graphs XXXIV. Five new topological indices for the
branching of tree like graphs. Theoret. Chem. Acta. 53, 355-375.
Balaban, A.T., 1982. Highly discriminating distance-based topological index. J.
Chem. Phys. Lett. 89, 399-404.
Balaban, A.T., 1983. Topological indices based upon topological distances in
molecular graphs. Pure Appl. Chem. 55, 199-206.
References
279
Balaban, A.T., 1986. Chemical Graphs 48: Topological index J for heteroatom
containing molecules taking into account periodicities of element properties.
MATCH Comm. Math. Comput. Chem. 21, 115-122.
Balaban, A.T., 1987. Numerical modeling of chemical structures. Local graph
invariants and topological indices. In: King, R.B., Rouvray, D.H., (Eds.), Graph
theory and topology in chemistry. Elsevier, Amsterdam, pp. 159-176.
Balaban, A.T., 1995. Local (atomic) and global (molecular) graph-theoretical
descriptors. SAR & QSAR Environ. Res. 3, 81-95.
Balaban, A.T., Beteringhe, A., Constantinescu, T., Filip, P.A., Ivanciuc, O., 2007. Four
new topological indices based on the molecular path code. J. Chem. Inf. Model.
47, 716-731.
Balaban, A.T., Ciubotariu, D., Ivanciuc, O., 1990. Design of topological indices. Part 2.
Distance measure connectivity indices. MATCH Commun. Math. Comput. Chem.
25, 41-70.
Balaban, A.T., Diudea, M.V., 1993. Real number vertex invariants: regressive
distance sums and related topological indices. J. Chem. Inf. Comput. Sci. 33,
421-428.
Balaban, A.T., Harary, F., 1968. Chemical graphs. V. Enumeration and proposed
nomenclature of benzenoid catacondensed polycyclic aromatic hydrocarbons.
Tetrahedron. 24, 2505-2516.
Balaban, A.T., Mills, D., Ivanviuc, O., Basak, S.C., 2000. Reverse Wiener indices.
Croat. Chem. Acta 73, 923-941.
Balaban, T.S., Balaban, A.T., Bonchev, D., 2001. A topological approach to
predicting properties of infinite polymers. Part VI. rational formulas for the
normalized Wiener index and a comparison with index J. J. Mol. Str.
(THEOCHEM) 535, 81-92.
Baldi, P., Bruank, S., Chauvin, Y., Andersen, C. A. F., Nielsen, H., 2000. Assessing
the accuracy of prediction algorithms for classification: An overview.
Bioinformatics. 16, 412-424.
Balram, P., 2004. Drug discovery: myth and reality. Curr. Sci. 87, 847-848.
Ban, T.A., 2006. The role of serendipity in drug discovery. Dialogues Clin. Neurosci.
8, 335-344.
References
280
Bangov, I., 1990. Computer-assisted structure generation from a gross formula. 3.
Alleviation of the combinatorial problem. J. Chem. Inf. Comput. Sci. 30, 277-
289.
Barysz, M., Jashari, G., Lall, R. S., Srivastava, V. K., Trinajstic, N., 1983. On the
distance matrix of molecules containing heteroatoms. In: King R.B. (Ed.),
Chemical Applications of Topology and Graph Theory, Elsevier, Amsterdam,
pp 222-230.
Basak, S.C., Bertelsen, S., Grunwald, G.D., 1994. Application of Graph Theoretical
Parameters in Quantifying Molecular Similarity and Structure-Activity
Relationships. J. Chem. Inf. Comput. Sci. 34, 210-276.
Basak, S.C., Grunwald, G.D. Niemi, G.J., 1997a. Use of graph-theoretic and
geometrical molecular descriptors in structure-activity relationships. In:
Balaban, A.T., (Ed.), From Chemical Topology to Three Dimensional
Molecular Geometry, Plenum Press, New York, pp. 73-116.
Basak, S.C., Gute, B.D., Balaban, A.T., 2004. Interrelationship of major topological
indices evidenced by clustering. Crot. Chem. Acta. 77, 331-344.
Basak, S.C., Gute, B.D., Grunwold, G.D., 1997b. Use of topostructural, topochemical
and geometric parameters in the prediction of vapor pressure: a hierarchical
QSAR approach. J. Chem. Inf. Comput. Sci. 37, 651-655.
Basak, S.C., Magnuson, V.R., 1983. Molecular topology and narcosis: A quantitative
structure-activity relationship study of alcohols using complementary
information content (CIC). Arzneim. Forsch. Drug Res. 33, 501-503.
Basak, S.C., Mills, D., Hawkins, D.M., El-Masri, H.A., 2002. Prediction of tissue:air
partition coefficients, a comparison of structure-based and property-based
methods. SAR QSAR Environ. Res. 13, 649-665.
Basak, S.C., Niemi, G.J., Veith, G.D., 1990. Optimal characterization of structure for
prediction of properties. J. Math. Chem. 4, 185-205.
Basak, S.C., Nikolic, S., Trinajstic, N., Amic, D., Beslo, D., 2000. QSPR modeling:
Graph connectivity indices versus line graph connectivity indices. J. Chem. Inf.
Comput. Sci. 40, 927-933.
Basak, S.C., Roy, A.B., Ghosh, J.J., 1980. Study of the structure-function relationship
of pharmacological and toxicological agents using information theory. In:
Avula, X.J.R., Bellman, R., Luke, Y.L., Rigler, A.K., (Eds.), Proceedings of the
References
281
Second International Conference on Mathematical Modelling. University of
Missouri-Rolla, pp.851-856.
Benigni, R., Bossa, C. 2008. Predictivity of QSAR. J. Chem. Inf. Model. 48(5), 971-
980.
Bentley, D.R., 2000. The human genome project-an overview. Med. Res. Rev. 20,
189-196.
Bertz, S.H., 1981. The first general index of molecular complexity. J. Am. Chem. Soc.
103, 3599-3601.
Bertz, S.H., 1988. Branching in graphs and molecules. Discrete Appl. Math. 19, 41-
70.
Bharath, E.N., Manjula, S.N., Vijaychand, A., 2011. In silico drug design tool for
overcoming the innovation deficit in the drug discovery process. Int. J. Pharm.
Sci. 3, 8-12.
Bohari, M.H., Srivastava, H.K., Sastry, G.N., 2011. Analogue-based approaches in
anti-cancer compound modelling: the relevance of QSAR models. Org. Med.
Chem. Lett., 1, 1–12.
Bonacich, P., 1972. Factoring and weighing approaches to clique identification. J.
Math. Sociol. 2, 113-120.
Bonacich, P., 2007. Some unique properties of eigenvector centrality. Soc. Netw. 29,
555-564.
Bonchev, D., 1983. Information Theoretic Indices for Characterization of Chemical
Structures. Research Study Press, Chichester.
Bonchev, D., 1995. Topological orders in a molecule 1. molecular branching
revisited. J. Mol. Str. (Theochem) 336, 137-156.
Bonchev, D., 1997. Novel indices for the topological complexity of molecules. SAR
QSAR Environ. Res. 7, 23-43.
Bonchev, D., 1999. Overall connectivity and molecular complexity: a new tool for
QSPR/QSAR. In: Devillers, J., Balaban, A.T., (Eds.), Topological Indices and
Related Descriptors in QSAR and QSPR, Gordon and Breach, The Netherlands,
pp. 361-402.
Bonchev, D., 2000. Overall connectivities/topological complexities: a new powerful
tool for QSPR/ QSAR. J. Chem. Inf. Comput. Sci., 40, 934-941.
References
282
Bonchev, D., 2001a. Overall connectivity-a next generation molecular connectivity. J.
Mol. Graph. Model. 20, 65-75.
Bonchev, D., 2001b. The overall Wiener index- a new tool for characterization of
molecular topology. J. Chem. Inf. Comput. Sci. 41, 582-592.
Bonchev, D., 2005. My life long journey in mathematical chemistry. Internet J. Mol.
Des. 4, 434-490.
Bonchev, D., Balaban, A.T. Liu, X., Klein, D.J., 1994. Molecular cyclicity and
centricity of polycyclic graphs. I. Cyclicity based on resistance distances or
reciprocal distances. Int. J. Quant. Chem. 50, 1-20.
Bonchev, D., Balaban, A.T., Mekenyan, O., 1980. Generalization of the graph center
concept, and derived topological centric indexes. J. Chem. Inf. Comput. Sci. 20,
106-113.
Bonchev, D., Buck, G.A., 2007. From molecular to biological structure and back. J.
Chem. Inf. Model. 47, 909-917.
Bonchev, D., Kamenski, D., Kamenska, V., 1976. Symmetry and information content
of chemical structures. Bull. Math. Biol. 38, 119-133.
Bonchev, D., Knop, J.V. Trinajstic, N., 1979. Mathematical models of branching.
MATCH Commun. Math. Comput. Chem. 6, 21-47.
Bonchev, D., Mekenyan, O., Kamenska, V., 1992. A topological approach to the
modeling of polymer properties: the tempo method. J. Math. Chem. 11, 107-132.
Bonchev, D., Trinajstic. N., 1977. Information theory, distance matrix and branching.
J. Chem. Phys. 67, 4517-4533.
Borodina, Y., Filimonov, D., Poroikov, V., 1998. Computer-aided estimation of
synthetic compounds similarity with endogenous bioregulators. Quant. Struct.
Act. Relat. 17, 459-464.
Breiman, L., 2001. Random forests. Machine Learning. 45, 5-32.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984. Classification and
regression trees, Chapman & Hall, CRC press, Boca Raton, Florida.
Brillouin, L., 1962. Science and information theory, Academic Press, New York.
Brodley, C.E., Utgoff, P.E., 1995. Multivariate decision trees. Machine Learning. 19,
45-77.
References
283
Broto, P., Moreau, G., Vandycke, C., 1984a. Molecular structures: perception,
autocorrelation descriptor and sar studies. Autocorrelation descriptor. Eur. J.
Med. Chem. 19, 66-70.
Broto, P., Moreau, G., Vandycke, C., 1984b. Molecular structures: perception,
autocorrelation descriptor and sar studies. Use of the autocorrelation descriptor in
the qsar study of two non-narcotic analgesic series. Eur. J. Med. Chem. 19, 79-84.
Brualdi, R., Ryser, H.J., 1991. Combinatorial matrix theory. Cambridge University
Press, New York.
Bruce, C. L., Melville, J. L., Pickett, S. D. and Hirst, J. D., 2007. Contemporary
QSAR Classifiers Compared. J. Chem. Inf. Model. 47, 219-227.
Bruckler, F.M., Doslic, T., Graovac, A., Gutman, I., 2011. On a class of distance
based molecular structure descriptors. Chem. Phy. Lett. 503, 336-338.
Burden, F.A., 1997. A chemically intuitive molecular index based on the eigenvalues
of a modified adjacency matrix. Quant. Struct. Act. Relat. 16, 309-314.
Burden, F.R., 1989. Molecular identification number for substructure searches. J.
Chem. Inf. Comput. Sci. 29, 225-227.
Burden, F.R., Polley, M.J., Winkler, D.A., 2009. Toward novel universal descriptors:
charge fingerprints. J. Chem. Inf. Model. 49, 710-715.
Cammarata, A., Menon, G.K., 1976. Pattern recognition. Classification of therapeutic
agents according to pharmacophores. J. Med. Chem. 19, 739-748.
Cao, C., Jiang, L., Yuan, H., 2003. Eigenvalues of the bond adjacency matrix
extended to application in physicochemical properties of alkanes. Internet
Electron. J. Mol. Des. 2, 621-641.
Cao, C., Yuan, H., 2001. Topological indices based on vertex, distance, and ring: on
the boiling points of paraffins and cycloalkanes. J. Chem. Inf. Comput. Sci. 41,
867-877.
Cao, C., Yuan, H., 2002. A modified distance matrix to distinguish cis/trans isomers
of cycloalkanes. Internet Electron. J. Mol. Des. 1, 401-409.
Carugo, O., 2007. Detailed estimation of bioinformatics prediction reliability through
the fragmented prediction performance plots. BMC Bioinformatics. 8, 380.
Available at: http://www.biomedcentral.com/1471-2105/8/380 (Accessed on
23/03/2012).
References
284
Casanola-Martin, G.M., Marrero-Ponce, Y., Tareq, M., Khan, H., Ather, A., Khan,
K.M., Torrens, F., Rotondo, R., 2007. Dragon method for finding novel
tyrosinase inhibitors: Biosilico identification and experimental in vitro assays.
Eur. J. Med. Chem. 42, 1370-1381.
Castro E.A., Tueros M., Toropov A.A., 2000. Maximum topological distances based
indices as molecular descriptors for QSPR 2-Application to aromatic
hydrocarbons Computers and Chemistry. 24, 571-576.
Castro, E.A., Gutman, I., Marino, D., Peruzzo, P., 2002. Upgrading the Wiener index.
J. Serb. Chem. Soc. 67, 647-651.
Chabner, B.A., 1988. In Celebration of a Nobel Prize. J. Nat. Cancer Institute. 80,
1512-1513.
Chekmarev D.S., Kholodovych V., Balakin K.V., Ivanenkov, Y., Ekins S., Welsh,
W.J., 2008. Shape signatures: new descriptors for predicting cardiotoxicity in
silico. Chem. Res. Toxicol. 21, 1304-1314.
Cheng, A., Merz, K.M., Jr., 2005. Prediction of aqueous solubility of a diverse set of
compounds using quantitative structure-property relationships. J. Med. Chem.
46, 3572-3580.
Cheng, Y.Y., Yuan, H., 2006. Quantitative study of electrostatic and steric effects on
physicochemical property and biological activity. J. Mol. Graph. Model. 24,
219-226.
Cho, S. H., Warit, S., Wan, B., Hwang, C. H., Pauli, G. F., Franzblau, S.G., 2007.
Low-oxygen-recovery assay for high-throughput screening of compounds
against non-replicating Mycobacterium tuberculosis. Antimicrob. Agents
Chemother. 51, 1380– 1385.
Clark, D.E., 2006. What has computer-aided molecular design ever done for drug
discovery? Expert Opin. Drug Discov. 1, 103-110.
Clarke, S., Tamanoi, F., 2004. Fighting cancer by disrupting C-terminal methylation
of signaling proteins, J. Clin. Invest. 113(4), 513-515.
Conn, P.J., 2005. Drug discovery in the 21st century: a sea change in knowledge,
technology and collaboration. Lens-a new way of looking at science. Volume 3,
Number 1, Vanderbilt medical center, Vanderbilt University, Nashville,
Tennessee, pp. 2-13.
References
285
Consonni V., Todeschini R., 2001. Rational Approaches to Drug Design, Hottje H.
D., Sippl.W., eds Barcelona: Prous Science, Spain. pp 235-240.
Consonni V., Todeschini R., Pavan M.J., 2002a. Structure/response correlations and
Similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel
3D molecular descriptors, J. Chem. Inf. Comp. Sci. 42, 682-692.
Consonni V., Todeschini R., Pavan M.J., Gramatica P., 2002b. Structure/response
correlations and Similarity/diversity analysis by GETAWAY descriptors. 2.
Application of the novel 3D molecular descriptor to QSAR/QSPR studies. J.
Chem. Inf. Comp. Sci. 42(3), 693-705.
Cortes, C., Vapnik, V., 1995. Support-vector networks. Machine. Learning. 20, 273-
293.
Courvoisier, S., 1956. Pharmacodynamic basis for the use of chlorpromazine in
psychiatry. J. Clin. Exp. Psychopathol. 17, 25-37.
Cragg, G.M., Newman, D.J., 2005. Biodiversity: A continuing source of novel drug
leads. Pure Appl. Chem. 77, 7-24.
Cramer, R.D., Patterson, D.E., Bunce, J.D., 1988. Comparative molecular-field
analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins.
J. Am. Chem. Soc. 110, 5959-5967.
Crecente, R.P., Latorre, C.H., 1993. Pattern recognition analysis applied to
classification of honeys from two geographic origins. J. Agric. Food Chem. 41,
560-564.
Cross, S., Cruciani, G., 2010. Molecular fields in drug discovery: getting old or
reaching maturity? Drug Discov. Today. 15, 23-32.
Crum-Brown, A., Fraser, T.R., 1868. On the connection between chemical
constitution and physiological action. Part 1. On the Physiological action of the
salts of the ammonium bases, derived from strychnia, brucia, thebaia, codeia,
morphia, and nicotia. Trans. R. Soc. Edinburg. 25, 151-203.
Cuadrado, M.U., Ruiz, I.L., Gomez-Nieto, M.A., 2006. Refinement and use of the
approximate similarity in QSAR models for benzodiazepine receptor ligands. J.
Chem. Inf. Model. 46, 2022-2029.
Dai, Q., Choy E., Chiu V., Romano J., Slivka S.R., Steitz S.A., Michaelis S, Philips
M.R., 1998. Mammalian prenylcysteine carboxyl methyltransferase is in the
endoplasmic reticulum, Biol. Chem. 273, 15030–15034.
References
286
Dancoff, S.M., Quastler, H., 1953. Essays on the use of information theory in biology.
University of Illinois, Urbana.
Das, K.C., Trinajstić, N., 2010. Comparison between first geometric–arithmetic index
and atom-bond connectivity index. Chem. Phy. Lett. 497, 149-151.
Das, K.C., Trinajstić, N., 2011. Relationship between the eccentric connectivity index
and Zagreb indices. Comput. Math. Appl. 62, 1758-1764.
Das, R., Sengur, A., 2010. Evaluation of ensemble methods for diagnosing of valvular
heart disease. Expert Syst. Appl. 37, 5110-5115.
Deconinck, E., Coomans, D., Vander Heyden, Y., 2007. Exploration of linear
modelling techniques and their combination with multivariate adaptive
regression splines to predict gastro-intestinal absorption of drugs. J. Pharm.
Biomed. Anal. 43, 119-130.
Dehmer, M., Barbarini, N., Varmuza, K., Graber, A., 2010. Novel topological
descriptors for analyzing biological networks. BMC Struct. Biol. 10, 18-27.
Dehmer, M., Grabner, M. Varmuza, K., 2012. Information indices with high
discriminative power for graphs. PLoS ONE. 7, e31214.
Dejaegher, B., Dhooghe, L., Goodarzi, M., Apers, S., Pieters, L., Heyden, Y.V., 2011.
Classification models for neocryptolepine derivatives as inhibitors of the
haematin formation. Anal. Chim. Acta. 705, 98-110.
Dejulian-Ortiz, J.V., De Gregorio-Alapont, C., Rios-Santamarina, J., Garcia-
Domenech, R., Galvez, J., 1998. Prediction of properties of chiral compounds
by molecular topology. J. Mol. Graph. Model. 16, 14-18.
Deng, H., 2011. On the sum-Balaban index. MATCH Commun. Math. Comput.
Chem. 66, 273-284.
Denny, W.A., 2012. The design and development of anti-cancer drugs. Available at:
http://nzic.org.nz/ChemProcesses/biotech/12J.pdf.
Devillers, J., Balaban, A.T., 1999. Topological indices and related descriptors in
QSAR and QSPR. Gordon and Breach Science Publishers, Amsterdam.
Diudea, M.V. Randic, M., 1997. Matrix operator, W M1, M2, M3 and Schultz-type
indices. J. Chem. Inf. Comput. Sci. 37, 1095-1100.
Diudea, M.V., 1994. Molecular topology 16. Layer matrices in molecular graphs. J.
Chem. Inf. Comput. Sci. 34, 1064-1071.
References
287
Diudea, M.V., 1996a. Wiener and hyper-Wiener numbers in a single matrix. J. Chem.
Inf. Comput. Sci. 36, 833-836.
Diudea, M.V., 1996b. Walk numbers eWM: Wiener type numbers of higher rank. J.
Chem. Inf. Comput. Sci. 36, 535-540.
Diudea, M.V., 1997a. Cluj matrix invariants. J. Chem. Inf. Comput. Sci. 37, 300–305.
Diudea, M.V., 1997b. Cluj matrix, CJU: source of various graph descriptors. MATCH
Commun. Math. Comput. Chem. 35, 169-183.
Diudea, M.V., 1997c. Indices of reciprocal properties or Harary indices. J. Chem. Inf.
Comput. Sci. 37, 292-299.
Diudea, M.V., 2006. Omega polynomial. Carpathian J. Math. 22, 43-47.
Diudea, M.V., Florescn, M.S., Khadikar, P.V., 2006. Molecula parameters, Topology
and its Applications. EFICON Press. Bucarest.
Diudea, M.V., Gutman, I., Lorentz, J., 2002. Topological matrices, In: Molecular
topology, Nova Science Publishers, Huntington, New York, pp. 11-52.
Diudea, M.V., Ilic, A., Varmuza K, Dehmer, M., 2011. Network analysis using a
novel highly discriminating topological index. Complexity. 16, 32-39.
Diudea, M.V., Katona, G., Lukovits, I., Trinajstic, N., 1998. Detour-Cluj versus
Detour indices. Croat. Chem. Acta 71, 459-471.
Diudea, M.V., Minailiuc, O., Balaban, A.T., 1991. Molecular topology IV. regressive
vertex degrees new graph invariants and derived topological indices. J. Comput.
Chem. 12, 527-535.
Dobrynin, A.A., Gutman, I., 1994. On a graph invariant related to the sum of all
distances in a graph. Publ. Inst. Math. (Beograd). 56, 18-22.
Dong, H., Zhou B., Trinajstić, N., 2011. A novel version of the edge-Szeged index.
Croat. Chem. Acta. 84, 543-545.
Doslic T., Reti T., 2012. Novel degree-based molecular descriptors with increased
discriminating power. Acta Polytechnica Hungarica. 9, 17-30.
Doslic, T., Reti, T., Vukicevic, D., 2011. On the vertex degree indices of connected
graphs. Chem. Phy. Lett. 512, 283-286.
References
288
Doslic, T., Saheli, M., Vukicevic, D., 2010. Eccentric connectivity index: extremal
graphs and values. Iranian J. Math. Chem. 1, 45-56.
Downward, J., 2003. Targeting RAS signaling pathways in cancer therapy. Nat. Rev.
Cancer. 3, 11-22.
Drews, J., 2000. Drug Discovery: A historical perspective. Science. 287, 1960-1964.
Drie, J.H.V., 2007. Computer-aided drug design: the next 20 years. J. Comput. Aided
Mol. Des. 21, 591–601.
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A. J., Vapnik, V., 1997. Support
vector regression machines. In: Mozer, M.C., Jordan, M.I., and Petsche, T.,
(Eds.), Advances in neural information processing systems, MIT Press,
Cambridge, pp. 155-161.
Duch, W., Swaminathan, K., Meller, J., 2007. Artificial intelligence approaches for
rational drug design and discovery. Curr. Pharm. Des. 13, 1497-1508.
Duchowicz, P. R., Castro, E. A., Fern´andez, F. M., 2008. Application of a novel
ranking approach in QSPR-QSAR. J. Math. Chem. 43, 620-636.
Duchowicz, P., Sinani, R.G., Castro, E.A., Toropov, A.A., 2003. Maximum topological
distance based indices as molecular descriptors for QSPR. V- Modeling the free
energy of hydrocarbons. Indian J. Chem. 42A, 1354-1359.
Dudek, A.Z., Arodz, T., Galvez, J., 2006. Computational methods in developing
quantitative structure-activity relationship: a review. Comb. Chem. High
Throughput Screen. 9, 213-228.
Dunn, W.J., Wold, S., 1980. Relationships between chemical structure and biological
activity modeled by SIMCA pattern recognition. Bioorg. Chem. 9, 505-523.
Durant, J.L., Leland, B.A., Henry, D.R., Nourse, J.G., 2002. Reoptimization of MDL
keys for use in drug discovery. J. Chem. Info. Comput. Sci. 42, 1273-1280.
Dureja, H., Gupta, S., Madan, A.K., 2008. Predicting anti-HIV-1 activity of 6-
arylbenzonitriles: Computational approach using superaugmented eccentric
connectivity topochemical indices. J. Mol. Graph. Model. 26, 1020-1029.
Dureja, H., Madan A.K., 2006. Models for the prediction of h5-HT2A receptor
antagonistic activity of arylindoles: computational approach using topochemical
descriptors. J. Mol. Graph. Mod. 25, 373-379.
References
289
Dureja, H., Madan, A.K., 2005. Topochemical models for prediction of cyclin-
dependent Kinase 2 inhibitory activity of indole-2-ones. J. Mol. Mod. 11, 525-
531.
Dureja, H., Madan, A.K., 2007. Superaugmented eccentric connectivity indices: New-
generation highly discriminating topological descriptors for QSAR/QSPR
modeling. Med. Chem. Res. 16, 331-341.
Dureja, H., Madan, A.K., 2009. Predicting anti-HIV activity of dimethyl
aminopyridin-2-ones: Computational approach using topochemical descriptors.
Chem. Biol. Drug Des. 73, 258-270.
Dureja, H., Madan, A.K., 2012. Pendencity based descriptors for QSAR/QSPR, In:
Gutman, I., Furtula, B. (Eds.), Distance in Molecular Graphs-Applications,
Mathematical Chemistry Monographs No. 13, University of Kragujevac, pp. 55-
80.
Dutt, R., Madan, A.K., 2010. Improved superaugmented eccentric connectivity index
for QSAR/QSPR modelling part 1: Development and evaluation. Med. Chem.
Res. 19, 431-447.
Dutt, R., Madan, A.K., 2012a. Role of distance sum based molecular descriptors in
drug discovery process, In: Gutman, I., Furtula, B. (Eds.), Distance in Molecular
Graphs-Application, Mathematical Chemistry Monographs No.13, University of
Kragujevac, Serbia, pp. 225-252.
Dutt, R., Madan, A.K., 2012b. Predicting biological activity: computational approach
using novel distance based molecular descriptors. Comput. Biol. Med. 42, 1026-
1041.
Dutta, D., Guha, R., Wild, D., Chen, T., 2007. Ensemble feature selection: Consistent
descriptor subsets for multiple QSAR models. J. Chem. Inf. Model. 47, 989-997.
Ediz, S., 2010. Optoelectron. Computing Ediz eccentric connectivity index of an
infinite class of nanostar dendrimers. Adv. Mater. Rapid Commun. 4, 1847-
1848.
Ediz, S., 2012. On the Ediz eccentric connectivity index of a graph. Optoelectron.
Adv. Mater. Rapid Commun. 6, 664-667.
Ehrlich, P., 1957. Collected papers of Paul Ehrlich. In: Histology, biochemistry and
pathology, Himmelweit, F., (Ed.), Volume 1, Pergammon press, New York.
References
290
Ekins, S., Mestres, J., Testa, B., 2007. In silico pharmacology for drug discovery:
methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152, 9–20.
Eliasi, M., Iranmanesha, A., Gutman I., 2012. Multiplicative versions of first Zagreb
index. MATCH Commun. Math. Comput. Chem. 68, 217-230.
Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression
trees. J. Animal Ecol. 77, 802-813.
Engel, T., 2012. Cheminformatics in diverse dimensions. In: Banting, L., Clark, T.,
(Eds.), Drug Design Strategies: Computational Techniques and Applications,
Royal Society of Chemistry Publishing, Cambridge, United Kingdom, pp. 164-
179.
Enna, S.J., 2000. Drug stories of origins and uses. In: Stone, T., Darlington, G., (Eds.)
Pills, Potions and Poisons; How drugs work, Oxford University Press, New
York, pp. 492-493.
Espeso, V.G., Vara, J.J.M., Lazaro, B.R., Parcerisas, F.R., Plavsic, D., 2000. On the
Hosoya hyperindex and the molecular indices based on a new decomposition of
the Hosoya Z matrix. Croat. Chem. Acta. 73, 1017-1026.
Estrada, E., 1995. Edge adjacency relationships and a novel topological index related
to molecular volume. J. Chem. Inf. Comput. Sci. 35, 31-33.
Estrada, E., 1996. Spectral moments of the edge adjacency matrix in molecular
graphs. 1. definition and application to the prediction of physical properties of
alkanes. J. Chem. Inf. Comput. Sci. 36, 844-849.
Estrada, E., 1997. Spectral moments of the edge adjacency matrix in molecular
graphs. 2. molecules containing heteroatoms and QSAR applications. J. Chem.
Inf. Comput. Sci. 37, 320-328.
Estrada, E., 1998. Spectral moments of the edge adjacency matrix in molecular
graphs. 3. molecules containing cycles. J. Chem. Inf. Comput. Sci. 38, 23-27.
Estrada, E., 2000a. Characterization of 3D molecular structure. Chem. Phys. Lett. 319,
713-718.
Estrada, E., 2000b. Edge-connectivity indices in QSPR/QSAR studies. 2. Accounting
for long-range bond contributions. J. Chem. Inf. Comput. Sci. 40, 1042-1048
Estrada, E., 2000c. On the topological sub-structural molecular design TOSS-MODE
in QSPR/QSAR and drug design research. SAR QSAR Environ. Res. 11, 55-73.
References
291
Estrada, E., Guevara, N., Gutman, I., 1998a. Extension of edge connectivity index.
relationship to line graph indices and QSPR applications. J. Chem. Inf. Comput.
Sci. 38, 428-431.
Estrada, E., Gutman, I., 1996. A topological index based on distances of edges of
molecular graphs. J. Chem. Inf. Comput. Sci. 36, 850-853.
Estrada, E., Matamala, A.R., 2007. Generalized topological indices. modeling gas
phase rate coefficients of atmospheric relevance. J. Chem. Inf. Model. 47, 794-
804.
Estrada, E., Molina, E., 2001. Novel local (fragment–based) topological molecular
descriptors for QSPR/QSAR and molecular design. J. Mol. Graph. Model. 20,
54- 64.
Estrada, E., Patlewicz, G., Uriarte, E., 2003. From molecular graphs to drugs. A
review on the use of topological indices in drug design and discovery. Indian J.
Chem. 42A, 1315-1329.
Estrada, E., Ramirez, A., 1996. Edge adjacency relationships and molecular
topographic descriptors. definition and QSAR applications. J. Chem. Inf.
Comput. Sci. 36, 837-843.
Estrada, E., Rodriguez, L., 2000. Edge-connectivity indices in QSPR/QSAR studies. 1.
Comparison to other topological indices in QSPR studies. J. Chem. Inf. Comput.
Sci. 40, 1037-1041.
Estrada, E., Uriarte, E., 2001. Recent advances on the role of topological indices in
drug discovery research. Curr. Med. Chem. 8, 1573-1588.
Falzari, K., Zhu, Z., Pan, D., Liu, H., Hongmanee, P., Franzblau, S. G., 2005. In vitro
and in vivo activities of macrolide derivatives against Mycobacterium
tuberculosis. Antimicrob. Agents Chemother. 49, 1447-1454
Fath-Tabar, G., Furtula, B., Gutman, I., 2010. A new geometric-arithmetic index. J.
Math. Chem. 47, 471-486.
Feng, X.Z., Yang, J., Luo, F.L., Chen, J.Y., Zhong, X.P., 2006. Automatic modulation
recognition by support vector machines using wavelet kernel. J. Physics
Conference Series. 48, 1264-1267.
Ferguson, A.M., Heritage, T.W., Jonathon, P., Pack, S. E., Phillips, L., Rogan, J.,
Snaith, P.J., 1997. EVA: a new theoretically based molecular descriptor for use
in QSAR\QSPR analysis. J. Comput. Aid. Mol. Des. 11, 143-152.
References
292
Ferydoun, A., Ali, R. A., and Najmeh, M., 2008. Study on QSPR method for
theoretical calculation of heat of formation for some organic compounds. Afr. J.
Pure Appl. Chem. 2(1), 006-009.
Filip, P.A., Balaban, T.S., Balaban, A.T., 1987. A new approach for devising local
graph invariants: Derived topological indices with low degeneracy and good
correlation ability. J. Math. Chem. 1, 61-83.
Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann.
Eugenics. 7, 179-188.
Food and Drug Administration., 2004. Challenge and opportunity on the critical path
to new medical products. U.S Department of Health and Human Services.
Available at: www.fda.gov/downloads/ScienceResearch/Special Topics/Critical
PathInitiative/CriticalPathOpportunitiesReports/UCM113411.pdf
Forrest, S., 1993. Genetic algorithms: principles of natural selection applied to
computation. Science. 261, 872-878.
Free, S.M., Jr Wilson, J.W., 1964. A mathematical contribution to structure-activity
studies. J. Med. Chem. 7, 395-399.
Freeman, L.C., 1977. A set of measures of centrality based on betweenness.
Sociometry. 40, 35-41.
Freeman, L.C., 1979. Centrality in social networks: Conceptual clarification. Soc.
Netw. 1, 215-239.
Frey, R., Lesney, M.S., 2000. Anodynes and estrogens: The Pharmaceutical decade,
The Pharmaceutical century; Ten decades of Drug Discovery. Supplements to
American Chemical Society. 92-109.
Friedman, J.H., 1991. Multivariate adaptive regression splines. Annals Stat. 19, 1-67.
Furtula, B., Graovac, A., Vukicevic D., 2010. Augmented Zagreb index. J. Math.
Chem. 48, 370-380.
Galvez, J., Garcıa-Domenech, R., 2007. Improving the local vertex invariants in
alkane graphs through a standard molecular orbital approach. Chem. Phy. Lett.
449, 249-254.
Galvez, J., Garcia-Domenech, R., de Gregorio-Alapont, C., 2000. Indices of
differences of path lengths: novel topological descriptors derived from
electronic interferences in graphs. J. Comput. Aided Mol. Des. 14, 679-687.
References
293
Galvez, J., Garcia-Domenech, R., De-Julian-ortiz, J.V., Soler, R., 1995. Topological
approach to drug design. J. Chem. lnf. Comput. Sci. 35, 272-284.
Garcia, G.C., Ruiz, I.L., Gomez-Nieto, M.A., Doncel, J.A.C., Plaza, A.G., 2005.
From Wiener index to molecules. J. Chem. Inf. Model. 45, 231-238.
Gaspar, A., Reis, J., Kachler, S., Paoletta, S., Uriarte, E., Klotz, K-N., Moro, S., Borges,
F., 2012. Discovery of novel A3 adenosine receptor ligands based on chromone
scaffold. Biochem. Pharmacol. 84, 21-29.
Geary, R.C., 1954. The contiguity ratio and statistical mapping. Incorp. Statist. 5,
115-145.
Ghorbani, M., Hosseinzadeh, M.A., 2012. A new version of Zagreb indices. Filomat.
26, 93-100.
Ghorbani, M., Songhori, M., Gutman, I., 2012. Modified Narumi-Katayama index.
Kragujevac J. Sci. 34, 57-64.
Ghose, A.K., Crippen, G.M., 1987. Atomic physico-chemical parameters for three
dimensional-structure-directed quantitative structure-activity relationships. 2.
modeling dispersive and hydrophobic interactions. J. Chem. Inf. Comput. Sci.
27, 21-35.
Ginsberg, M.A., 2010. Tuberculosis drug development: Progress, challenges, and the
road ahead. Tuberculosis. 90, 162–167.
Go, M., Leow, J.L., Gorla, S.K., Schuller, A.P., Wang, M., Casey, P.J., 2010. Amino
derivatives of indole as potent inhibitors of isoprenylcysteine carboxyl
methyltransferase, J. Med. Chem. 53, 6838–6850.
Goel, A., Madan, A.K., 1995. Structure-Activity study on anti-inflammatory pyrazole
carboxylic acid hydrazide analogs using molecular connectivity indices. J.
Chem. Inf. Comput. Sci. 35, 510-514.
Golbraikh, A., Bonchev, D., Tropsha, A., 2001. Novel chirality descriptors derived
form molecular topology. J. Chem. Inf. Comput. Sci. 41, 147-158.
Golbraikh, A., Bonchev, D., Tropsha, A., 2002. Novel ZE-isomerism descriptors
derived from molecular topology and their application to QSAR analysis. J.
Chem. Inf. Comput. Sci. 42, 769-787.
References
294
Gombar, V.K., Kumar, A., Murthy, M.S., 1987. Quantitative structure-activity
relationships. Part IX. A modified connectivity index as structure quantifier.
Ind. J. Chem. 26B, 1168-1170.
Goodnow, R.A., 2006. Hit and lead identification: integrated technology-based
approaches. Drug Discov. Technol. 3, 367-375.
Gordon, M., Scantlebury, G.R., 1964. Non random polycondensation: statistical
theory of the substitution effect. Trans. Faraday Soc. 60, 604-621.
Goyal, R.K., Dureja, H., Singh, G., Madan, A.K., 2010. Models for antitubercular
activity of 5'-O-[N-Acylsulfamoyl]adenosines. Sci. Pharm. 78, 791-820.
Goyal, R.K., Singh, G., Madan, A.K., 2011. Models for anti-tumor activity of
bisphosphonates using refined topochemical descriptors. Naturwissenschaften
98, 871-887.
Gozalbes, R., Doucet, J.P., Derouin, F., 2002. Application of topological descriptors
in QSAR and drug design: history and new trends. Curr. Drug Targets Infect.
Disord. 2, 93-102.
Grover, M., Singh, B., Bakshi, M., Singh, S., 2000a. Quantitative structure-property
relationship in pharmaceutical research – Part 1. Pharm. Sci. Technol. Today.
3(1), 28-35.
Grover, M., Singh, B., Bakshi, M., Singh, S., 2000b. Quantitative Structure-Property
Relationship in Pharmaceutical Research – Part 2. Pharm. Sci. Technol. Today.
3(2), 50-57.
Guha, R., 2005. Methods to improve the reliability, validity and interpretability of
qsar models. PhD Thesis, Department of Chemistry, The Pennsylvania State
University, United States.
Guha, R., Ven Drie, J.H., 2008. Structure-activity landscape index: identifying and
quantifying activity cliffs. J. Chem. Inf. Model. 48, 646-658.
Guo, M., Xu, I., Hu, C.Y., Yu, S.M., 1997. Study on structure-activity relationship of
organic compounds - applications of a new highly discriminating topological
index. MATCH Commun. Math. Comput. Chem. 35, 185-197.
Gupta, M., Gupta, S., Dureja, H., Madan, A.K., 2011. Superaugmented eccentric
distance sum connectivity indices: novel highly discriminating topological
descriptors for QSAR/QSPR. Chem. Biol. Drug Des. 79, 38-52.
References
295
Gupta, S., Singh, M., Madan, A.K., 1999. Superpendentic index: a novel topological
descriptor for predicting biological activity. J. Chem. Inf. Comput. Sci. 39, 272-
277.
Gupta, S., Singh, M., Madan, A.K., 2000. Connective eccentricity index: a novel
topological descriptor for predicting biological activity. J. Mol. Graph. Model.
18, 18-25.
Gupta, S., Singh, M., Madan, A.K., 2001. Predicting anti- HIV activity:
computational approach using novel topological indices. J. Comput. Aided Mol.
Des. 15, 671-678.
Gupta, S., Singh, M., Madan, A.K., 2002a. Application of graph theory: relationship
of eccentric connectivity index and Wiener’s Index with anti-inflammatory
activity. J. Math. Anal. Appl. 266, 259-268.
Gupta, S., Singh, M., Madan, A.K., 2002b. Eccentric distance sum: A novel graph
invariant for predicting biological and physical properties. J. Math. Anal. Appl.
275, 386-401.
Gupta, S., Singh, M., Madan, A.K., 2003. Novel topochemical descriptors for
predicting anti-HIV activity. Indian J. Chem. 42A, 1414-1425.
Gutierrez, Y., Estrada, E., 1997. TOSS-MODE-Topological Sub–Structural Molecular
Design for Windows Version 4.0, Universidad de Santiago de Compostela,
Spain.
Gutierrez-Oliva, S., Joubert L., Adamo C., Bulat F.A., Zagal J.H., Toro-Labbe, A.,
2006. Bridging the gap between the topological and orbital description of
hydrogen bonding: the case of the formic acid dimer and its sulfur derivatives.
J. Phys. Chem. A. 110, 5102-5107.
Gutman, I., 1994a. Selected properties of the Schultz molecular topological index. J.
Chem. Inf. Comput. Sci. 34, 1087-1089.
Gutman, I., 1994b. Formula for the Wiener number of trees and its extension to
graphs containing cycles. Graph Theory Notes. 27, 9-15.
Gutman, I., Estrada, E., 1996. Topological indices based on line graph of the
molecular graph. J. Chem. Inf. Comput. Sci. 36, 541-543.
Gutman, I., Klavzar, S., 1995. An algorithm for the calculation of the Szeged index of
benzenoid hydrocarbons. J. Chem. Inf. Comput. Sci. 35, 1011-1014.
References
296
Gutman, I., Linert, W., Lukovits, I., Tomovic, Z., 2000. The multiplicative version of
the Wiener index. J. Chem. Inf. Comput. Sci. 40, 113-116.
Gutman, I., Polansky, O.E., 1987. Mathematical concepts in organic chemistry.
Chemie in unserer zeit. 21(2), 72.
Gutman, I., Randic, M., 1977. Algebraic characterization of skeletal branching.
Chem. Phys. Lett. 47, 15-19.
Gutman, I., Ruscic, B., Trinajstic, N., Jr Wilcox, C.F., 1975. Graph theory and
molecular orbitals XII. Acyclic polyenes. J. Chem. Phys., 62, 3399-3405
Gutman, I., Trinajstic, N., 1972. Graph theory and molecular orbitals. total pi-electron
energy of alternant hydrocarbons. Chem. Phys. Lett. 17, 535-538.
Gutman, I., Trinajstic, N., 1973. Graph theory and molecular orbitals. Topics Curr.
Chem. 42, 49-93.
Gutman, I., Vukicevic, D., Zerovnik, J., 2004. A class of modified Wiener indices.
Croat. Chem. Acta 77, 103–109.
Hall, L.H., Kier, L.B., 1990. Determination of topological equivalence in molecular
graphs from the topological state. Quant. Struct. Act. Relat. 9, 115-131.
Hall, L.H., Kier, L.B., 1995. Electrotopological state indices for atom types: a novel
combination of electronic, topological, and valence state information. J. Chem.
Inf. Comput. Sci. 35, 1039-1045.
Han, C., Wang, B., 2005. Factors that impact the developability of drug candidates: an
overview. In: Wang, B., Siahaan, T., Soltero, R., (Eds.), Drug delivery:
principles and applications, John Wiley and Sons. Hoboken, New Jersey, USA,
pp. 1-13.
Han, L., Wang, Y., Bryant, S.H., 2008. Developing and validating predictive decision
tree models from mining chemical structural fingerprints and high
throughoutput data in PubChem. BMC Bioinformatics. 9, 401 Available at:
http://www.biomedcentral.com/1471-2105/9/401 (Accessed on 25/03/2012).
Hancock, J.F., 2003. Ras proteins: different signals from different locations. Nat. Rev.
Mol. Cell. Biol. 4, 373–384.
Hann, M., Green, R., 1999. Cheminformatics- a new name for an old problem. Curr.
Opin. Chem. Biol. 3, 379-383.
References
297
Hansch, C., Fujita, T., 1964. ε-σ-π Analysis: A method for the correlation of
biological activity and chemical structure. J. Am. Chem. Soc. 86, 1616-1626.
Hansch, C., Unger, S.H., Forsythe, A.B., 1973. Strategy in drug design. Cluster
analysis as an aid in the selection of substituents. J. Med. Chem. 16, 1217 -1222.
Hansch, C.A., 1969. Quantitative approach to biochemical structure-activity
relationship. Acct. Chem. Res. 2, 232-239.
Hansen, P.J., Jurs, P.C., 1988. Chemical applications of graph theory - Fundamentals
and topological indices. J. Chem. Edu. 65, 574-580.
Harary, F., 1969. Graph Theory, Addison-Wesley, Reading, Massachusetts.
Hastie, T., Tibshirani, R., 1996. Discriminant analysis by Gaussian mixtures. J. Royal
Statist. Soc. B. 58, 155-176.
Hastie, T., Tibshirani, R., Friedman, J., 2003. The elements of statistical learning:
Data Mining, Interference, and Prediction. Springer, New York.
Haydel, S.E., 2010. Extensively Drug-Resistant Tuberculosis: A sign of the times and
an impetus for antimicrobial discovery. Pharmaceuticals. 3, 2268-2290.
Haykin, S., 1998. Neural Networks: A Comprehensive Foundation. Prentice Hall
International, Inc., Upper Saddle River NJ, USA.
Hemmateenejad, B., Yousefinejad, S., Mehdipur, A.R., 2011. Novel amino acid
indices based on quantum topological molecular similarity QTMS and their
application to QSAR study of peptides. Amino Acids. 40, 1169-1183.
Hemmer, M.C., Steinhauer, V., Gasteiger, J., 1999. Deriving the 3D structure of
organic molecules from their infrared spectra. Vibrat. Spect. 19, 151-164.
Herklots, H., 2000. Outsourcing the search for leads. Mod. Drug. Discov. 46-52.
Herman, R.B., 1971. Theory of hydrophobic bonding. I. The solubility of hydrocarbons
in water, within the context of the significant structure theory of liquids. J. Phys.
Chem. 75, 363-368.
Hert, J., Willett, P., Wilton, D.J., Acklin, P., Azzaoui, K., Jacoby, E., Schuffenhauer,
A., 2004. Comparison of fingerprint-based methods for virtual screening using
multiple bioactive reference structures. J. Chem. Inf. Comput. Sci. 44, 1177-
1185.
References
298
Hong, H., Xie, Q., Ge, W., Qian, F., Fang, H., Shi, L., Su, Z., Perkins, R., Tong,
W., 2008. Mold2, Molecular descriptors from 2D structure for
cheminformatics and toxicoinformatics. J. Chem. Inf. Model. 48, 1337-
1344.
Hopfinger, A.J., Wang, S., Tokarski, J.S., Jin, B., Albuquerque, M., Prakash, J., 1997.
Construction of 3D-QSAR models using the 4D QSAR analysis formalism. J.
Am. Chem. Soc. 119, 10509-10524.
Hosoya, H., 1971. Topological index; newly proposed quantity characterizing the
topological nature of structure of isomers of saturated hydrocarbons. Bull.
Chem. Soc. Jpn. 44, 2332-2337.
Hosoya, H., 1972. Topological index as a sorting device for coding chemical
structures. J. Chem. Doc. 12, 181-183.
Hosoya, H., 2007. Important mathematical structures of the topological index Z for
tree graphs. J. Chem. Inf. Model. 47, 744-750.
Hosoya, H., Hosoi, K., Gutman, I., 1975. A topological index for the total π-electron
energy. Proof of a generalized Huckel rule for an arbitrary network. Theor.
Chim. Acta. 38, 37-47.
Hosoya, H., Kawasaki, K., Mizutani, K., 1972. Topological index and thermodynamic
properties. I. Empirical rules on the boiling points of saturated hydrocarbons.
Bull. Chem. Soc. Jpn. 45, 3415.
Hosoya, H., Murakami, M., Gotoh, M., 1973. Distance polynomial and
characterization of a graph. Natl. Sci. Rept. Ochanomizu Univ. 24, 27-34.
Houston, J.G., Banks, M.N., Squibb, B.M., 2003. High-throughput screening for lead
discovery. In: Abraham, D.J., (Ed.), Burger’s Medicinal Chemistry and Drug
Discovery, 6th
edition, vol. 2, John Wiley and Sons, New York, pp. 37-69.
Hrycyna, C.A., Clarke, S., 1990. Farnesyl cysteine C-terminal methyltransferase
activity is dependent upon the STE14 gene product in Saccharomyces
cerevisiae, Mol. Cell. Biol. 10, 5071–5076.
Hrycyna, C.A., Sapperstein, S.K., Clarke, S., Michaelis, S., 1991. The Saccharomyces
cerevisiae STE14 gene encodes a methyltransferase that mediates C-terminal
methylation of a-factor and RAS proteins, EMBO J. 10, 1699–1709.
http//www.phrma.org/2013
http://www.cran.r-project.org. (Accessed on 15/03/13).
References
299
Hu, C.Y., Xu, L., 1996. On Highly Discriminating Molecular Topological Index. J.
Chem. Inf. Comput. Sci. 36, 82-90.
Hu, C.Y., Xu, L., 1997. Developing molecular identification numbers by an all-paths
method. J. Chem. Inf. Comput. Sci. 37, 311-315.
Hu, Q.N., Liang, Y.Z., Wang, Y.L., Xu C.J., Zeng, Z.D., Fang K.T., Peng, X.L.,
Hong, Y., 2003. External factor variable connectivity index. J. Chem. Inf.
Comput. Sci. 43, 773-778.
Huang, H.J., Yu, H.W., Chen, C.Y., Hsu, C.H., Chen, H.Y., Lee, K.J., Tsai, F.J.,
Chen, C.Y.C., 2010. Current developments of computer-aided drug design. J.
Taiwan Inst. Chem. Eng. 41, 623-635.
Hughes, J.P., Rees, S., Kalindjian S.B., Philpott, K.L., 2011. Principles of early drug
discovery. Br. J. Pharmacol. 162, 1239-1249.
Ilic, A., 2010. Eccentric connectivity index. In: Gutman, I., Furtula, B., (Eds.), Novel
Molecular Structure Descriptors - Theory and Applications II, Mathematical
Chemistry Monographs No. 9, University of Kragujevac, Kragujevac, pp. 139-
168.
Iranmanesh, A., Gutman, I., Khormali, O., Mahmiani, A., 2009. The edge versions of
the wiener index. MATCH Commun. Math. Comput. Chem. 61, 663-672.
Iranmanesh, A., Kafrani, A.S., Khormali, O., 2011. A new version of Hyper-Wiener
index. MATCH Commun. Math. Comput. Chem. 65, 113-122.
Ivanciuc, I., Klein, D.J., 2002. Computing Wiener index indices for virtual
combinatorial libraries generated from heteroatom containing building blocks. J.
Chem. Inf. Comput. Sci. 42, 8-22.
Ivanciuc, O., 2000. QSAR comparative study of Wiener descriptors for weighted
molecular graphs. J. Chem. Inf. Comput. Sci. 40, 1412-1422.
Ivanciuc, O., 2004. Similarity matrices quantitative structure-activity relationships for
anticonvulsant phenylacetanilides. Int. Elect. J. Mol. Des. 3, 426-442.
Ivanciuc, O., Balaban, A.T., 1994. Design of topological indices. Part. 8. path
matrices and derived molecular graph invariants. MATCH Commun. Math.
Comput. Chem. 30, 141-152.
Ivanciuc, O., Balaban, A.T., 1998a. Graph theory in chemistry. In: Schleyer, P.V.R.,
Allinger, N.L., Clark, T., Gasteiger, J., Kollman, P.A., Schaefer, III H.F.,
References
300
Schreiner, P.R. (Eds.) The encyclopedia of computational chemistry. John Wiley
& Sons, Chichester, pp. 1169-90.
Ivanciuc, O., Balaban, T.S., Balaban, A.T., 1993a. Design of topological indices. Part
4. reciprocal distance matrix, related local vertex invariants and topological
indices. J. Math. Chem. 12, 309-318.
Ivanciuc, O., Balaban, T.S., Balaban, A.T., 1993b. Chemical graphs with degenerate
topological indices based on information on distances. J. Math. Chem. 14, 21-
33.
Ivanciuc, O., Ivanciuc, T., Balaban, A.T., 1998b. Design of topological indices. Part
10. parameters based on electronegativity and covalent radius for the
computation of molecular graph descriptors for heteroatom containing
molecules. J. Chem. Inf. Comput. Sci. 38, 395-401.
Ivanciuc, O., Ivanciuc, T., Balaban, A.T., 2002. Quantitative structure-property
relationship evaluation of structural descriptors derived from the distance and
reverse Wiener matrices. Internet Electron. J. Mol. Des. 1, 467-487.
Ivanciuc, O., Ivanciuc, T., Klein, D.J., 2001. Quantitative structure-property
relationships generated with optimizable even/odd Wiener polynomial
descriptors. SAR QSAR Environ. Res. 12, 1-16.
Ivanciuc, O., Laidboeur, T., Carol-Bass, D., 1997. Degeneracy of topologic distance
descriptors for cubic molecular graphs: example of small fullerenes. J.
Chem. Inf. Comput. Sci. 37(3), 485–488.
Jack, D.B., 1997. A hundred years of aspirin. The Lancet. 350, 437-439.
Janezic, D., Lucic, B., Milicevic, A., Nikolic, J., Trinajstic, N., Vukicevic, D., 2007.
Hosoya matrices as the numerical realization of graphical matrices and derived
structural descriptors. Croat. Chem. Acta. 80, 271-276.
Jiang W., McDonald, D., Hope, T.J., Hunter, T., 1999. Mammalian Cdc7–Dbf4
protein kinase complex is essential for initiation of DNA replication. EMBO J.,
18, 5703–5713.
Jolliffe, I.T., 1982. A note on the use of principal components in regression. J. Royal
Stat. Soc. 31, 300-303.
Jones, W.P., Chin, Y.W., Kinghorn, A.D., 2006. The role of pharmacognosy in
modern medicine and pharmacy. Curr. Drug Targets. 7, 247-264.
References
301
Junkes, B.D.S., Castanho Amboni, R.D.D.M., Yunes, R.A., Heinzen, V.E.F., 2003.
Semiempirical topological index: a novel molecular descriptor for quantitative
structure-retention relationship studies. Internet Electron. J. Mol. Des. 2, 33-49.
Junxia, Z., Gaokeng, X., Jialiang, G., Longyi, R., Wei, C., Kun, Z., Pinghua, S.,
2011. Three-dimensional quantitative structure-activity relationships of
pyrrolopyridinone as cell division cycle kinase inhibitors by CoMFA and
CoMSIA. Journal of Molecular Modeling. 17(8), 2113-2130.
Kaczmarek, L.X., Peczynska-Czoch, W., Osiadacz, J.X., Mordarski, M.,
Sokalski, W.A., Boratynski, J., Marcinkowska, E., Galzman-Kusnierczyk, H.,
Radzikowski, C.X., 1999. Synthesis and cytotoxic activity of some novel indolo
[2,3-b] quiniline derivatives-DNA topoisomerase II inhibitors. Bioorg. Med.
Chem. 7, 2457-2464.
Kairemo, K., Erba, P., Bergstrom, K., Pauwels E. K. J, 2008. Nanoparticles in
Cancer. Current Radiopharmaceut. 1, 30-36.
Kaitin, K.I., 2010. Deconstructing the drug development process: the new face of
innovation. Clin Pharmacol Ther. 87(3):356-361.
Kapetanovic, I.M., 2008. Computer-aided drug discovery and development
(CADDD): in silico-chemico-biological approach. Chem. Biol. Interact. 171,
165–176.
Karmarkar, S., Khadikar P.V., Agrawal, V.K., Mathur, K.C., Mandoli, M., 2000.
Topological estimation of proton-ligand formation constants of potential
antitumour agents: salicylhydroxamic acid. Proc. Indian Acad. Sci. Chem. Sci.
112, 43-49.
Katritzky, A.R., Maran, U., Lobanov, V.S. and Karelson, M., 2000. Structurally
diverse quantitative structure-property relationship correlations of
technologically relevant physical properties. J. Chem. Inf. Comput. Sci. 40(1),
1-18.
Katritzky, A.R., Mu, L., Lobanov, V.S., Karelson, M., 1996. Correlation of boiling
points with molecular structure. 1. A training set of 298 diverse organics and a
test set of 9 simple inorganics. J. Phys. Chem. 100, 10400-10407.
Katritzky, A.R., Petrukhin, R. and Tatham, D., 2001. Interpretation of quantitative
structure - property and-activity relationships. J. Chem. Inf. Comput., Sci.,
41(3), 679-685.
References
302
Kezele, N., Klasinc, L., Knop, J., Ivanis, S., Nikolic, S., 2002. Computing the vertex-
connectivity index. Croat. Chem. Acta. 75, 651-661.
Khadikar, P.V., Deshpande, N.V., Kale, P.P., Dobrynin, A.A., Gutman, I., Domotor,
G., 1995. The Szeged index and an analogy with the Wiener index. J. Chem. Inf.
Comput. Sci. 35, 547-550.
Khadikar, P.V., Kale, P.P., Deshpande, N.V., Karmarkar, S., Agarwal, V.K., 2001a.
Novel PI indices of hexagonal chains. J. Math. Chem. 29, 143-150.
Khadikar, P.V., Karmarkar, S., Agarwal, V.K., 2001b. A novel PI Index and its
application to QSPR/QSAR studies. J. Chem. Inf. Comput. Sci. 41, 934-949.
Khan, F., Yadav, D.M., Maurya, A., Sonia, Srivastava, S.S., 2011. Modern methods
and web resources in drug design and discovery. Lett. Drug Des. Discov. 8,
469-490.
Kier, L.B., 1971. Molecular Orbital theory in Drug Research, New York: Academic.
Kier, L.B., 1985. Shape index from molecular graphs. Quant. Struct. Act. Relat. 4,
109-116.
Kier, L.B., 1986a. Distinguishing atom differences in a molecular graph shape index.
Quant. Struct. Act. Relat. 5, 7-12.
Kier, L.B., 1986b. Indexes of molecular shape from chemical graphs. Acta Pharm.
Jugosl. 36, 171-188.
Kier, L.B., 1986c. Shape indexes of orders one and three from molecular graphs.
Quant. Struct. Act. Relat. 5, 1-7.
Kier, L.B., 1989. An index of molecular flexibility from kappa shape attributes.
Quant. Struct. Act. Relat. 8, 221-224.
Kier, L.B., Hall, L.H., 1976a. Molecular connectivity in chemistry and drug research.
Academic Press, New York.
Kier, L.B., Hall, L.H., 1976b. Molecular connectivity VII: specific treatment of
heteroatoms. J. Pharm. Sci. 65, 1806-1809.
Kier, L.B., Hall, L.H., 1986. Molecular connectivity in structure activity analysis,
Research Studies Press, John Wiley & Sons, Chichester, United Kingdom.
Kier, L.B., Hall, L.H., 1990. An electrotopological state index for atoms in molecules.
Pharm. Res. 7, 801-807.
References
303
Kier, L.B., Hall, L.H., 1999. The electrotopological state: Structure modeling for
QSAR and database analysis. In: Devillers, J., Balaban, A.T., (Eds.),
Topological Indices and Related Descriptors in QSAR and QSPR. Gordon and
Breach Science Publishers, The Netherlands, pp. 491-562.
Kier, L.B., Hall, L.H., Murray, W.J., Randic, M., 1975a. Molecular connectivity I:
relationship to nonspecific local anesthesia. J. Pharm. Sci. 64, 1971-1974.
Kier, L.B., Hall, L.H., Murray, W.J., Randic, M., 1975b. Molecular Connectivity II:
Relationship to water solubility and boiling point. J. Pharm. Sci. 64, 1975-1977.
Kier, L.B., Murray, W.J., Hall, L.H., 1975c. Molecular connectivity. 4. Relationships
to biological activities. J. Med. Chem. 18, 1272-1274.
Kim, D., Hong, S.I., Lee, D.S., 2006. Triazoloquinazolines as human A3 adenosine
receptor antagonists: a QSAR study. Int. J. Mol. Sci. 7, 485-496.
King, A., Selak, M.A., Gottlieb, E. 2006a. Succinate dehydrogenase and fumarate
hydratase: linking mitochondrial dysfunction and cancer. Oncogene. 25, 4675-
4682.
King, A.J., Patrick, D.R., Batorsky, R.S., Ho, M.L., Do, H.T., Zhang, S.Y., Kumar,
R., Rusnak, D.W., Takle, A.K., Wilson, D.M., Hugger, E., Wang, L., Karreth,
F., Lougheed, J.C., Lee, J., Chau, D., Stout, T.J., May, E.W., Rominger, C.M.,
Schaber, M.D., Luo, L., Lakdawala, A.S., Adams, J.L., Contractor, R.J.,
Smalley, K.S.M., Herlyn, M., Morrissey, M.M., Tuveson, D.A., Huang, P.S.,
2006b. Demonstration of a genetic therapeutic index for tumors expressing
oncogenic BRAF by the kinase inhibitor SB-590885. Cancer Res. 66, 11100-
11105.
King, J.W., 1989. A Z-weighted information content index. Int. J. Quantum Chem.
Quant. Biol. Symp. 16, 165-170.
King, R. B. 1983. Chemical applications of topology and graph theory. Elsevier,
Amsterdam.
Klein, C.T., Kaiser, D., Ecker, G., 2004. Topological distance based 3D descriptors
for use in QSAR and diversity analysis. J. Chem. Inf. Comput. Sci. 44, 200-209.
Klein, D.J., Ivanciuc, O., 2001. Graph cyclicity, excess conductance, and resistance
deficit. J. Math. Chem. 30, 271-287.
Klein, D.J., Lukovits, I., Gutman, I., 1995. On the definition of the hyper-Wiener for
cycle-containing structures. J. Chem. Inf. Comput. Sci. 35, 50-52.
References
304
Klein, D.J., Randic, M., 1993. Resistance distance. J. Math. Chem. 12, 81-95.
Kline, N.S., 1958. Clinical experience with iproniazid (MARSILID). J. Clin. Exp.
Psychopathol. 19, 72-78.
Klopman, G., Raychoudhary, C., 1988. A novel approach to the use of graph theory in
structure-activity relationship studies: Applications to the qualitative evaluation of
mutagenicity in a series of non-fused ring aromatic compounds. J. Comput.
Chem. 9, 232-243.
Kmentova, I. Sutherland, H. S., Palmer, B. D., Blaser, A., Franzblau, S.G., Wan, B.,
Wang, Y., Ma, Z., Denny, W.A., Thompson, A. M., 2010. Synthesis and
structure-activity relationships of aza and diazabiphenyl analogues of
antitubercular drug (6S)-2-Nitro-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-
dihydro-5H-imidazo[2,1-b][1,3]oxazine (PA-824). J. Med. Chem. 53(23), 8421-
8439.
Kniaz, D., 2000. Drug discovery adopts factory model. Mod. Drug. Discov. 5, 67-72.
Kohonen, T., 1994. Self organizing maps. In: Kohonen T., Huang T.S., Schroeder
M.R., (Eds.), Springer Series in Information Sciences. Springer, Heidelberg,
Germany.
Kola, I., Landis, J., 2004. Can the pharmaceutical industry reduce attrition rates? Nat.
Rev. Drug Discov. 3, 711-715.
Kolibaba, K.S., Druker B.J., 1997. Protein Tyrosine Kinase and Cancer. Biochimica
et Biophysica Acta. 1333, 217–248.
Konstantinova, E.V., Paleev, A.A., 1990. Sensitivity of topological indices of
polycyclic graphs. Vychisl. Sistemy. 136, 38-48.
Konstantinova, E.V., Skorobogatov, V.A., Vidyuk, M.V., 2003 Application of
information theory in chemical graph theory. Ind. J. Chem. 42A, 1227-1240.
Kotsiantis, S.B., 2007. Supervised machine learning: A review of classification
techniques. Informatica. 31, 249-268.
Koul, A., Arnoult, E., Lounis, N., Guillemont, J., Andries, K., 2011. The challenge of
new drug discovery for tuberculosis. Nature. 469, 483-490.
Kovesdi, I., Dominguez-Rodriguez, M.F., Orfi, L., Naray-Szabo, G., Varro, A., Papp,
J.G., Matyus, P., 1999. Application of neural networks in structure–activity
relationships. Med. Res. Rev. 19, 249-269.
References
305
Kowalski, E.R., Bender, C.F., 1974. The application of pattern recognition to
screening prospective anticancer drugs. adenocarcinoma biological activity test.
J. Am. Chem. Soc. 96, 916-918.
Kubinyi, H., Folkers, G., Martin, Y.C., 1998. 3D-QSAR in drug design—recent
advances. Perspect Drug Disc Design. 12: R5–R7.
Kuhlmann, J., 1997. Drug research: from the idea to the product. Int. J. Clin.
Pharmacol. Ther. 35, 541-552.
Kuhn, R., 1958. The treatment of depressive states with G22355 (imipramine
hydrochloride). Am. J. Psychiatry. 115, 459-464.
Kumar, V., Madan, A.K., 2005. Application of graph theory: prediction of glycogen
synthase kinase-3 inhibitory activity of thiadiazolidinones as potential drugs for
the treatment of Alzheimer’s disease. Eur. J. Pharm. Sci. 24, 213-218.
Kumar, V., Madan, A.K., 2006. Application of graph theory: Prediction of cytosolic
phospholipase A2 inhibitory activity of propan-2-ones. J. Math. Chem. 39, 511-
521.
Kumar, V., Madan, A.K., 2007a. Application of graph theory: Models for prediction
of carbonic anhydrase inhibitory activity of sulfonamides. J. Math. Chem. 42,
925-940.
Kumar, V., Madan, A.K., 2007b. Predicting anti-allergic activity of 4-oxopyrimido
[4,5-b]quinolines: Computational approach using topochemical indices. Med.
Chem. Res. 16, 88-99.
Kumar, V., Madan, A.K., 2007c. Prediction of the agonist allosteric enhancer activity
of thiophenes with respect to human a1 adenosine receptors using topological
indices. Pharm. Chem. J. 41, 140-144.
Kumar, V., Sardana, S., Madan, A.K., 2004. Predicting anti-HIV activity of 2,3-
diaryl-1,3-thiazolidin-4-ones : computational approach using reformed eccentric
connectivity index. J. Mol. Model. 10, 399-407.
Lago, L. D., D’Hondt, V., Awada, A., 2008. Selected combination therapy with
sorafenib: areview of clinical data and perspectives in advanced solid tumors.
The Oncologist, 13, 845– 858.
Lailong, M., Chengjun, F., 2004. Novel connectivity index of edge valence and its
application. J. Chem. Indust. Engg. 55, 531-540.
References
306
Lajiness, M.S., 1990. Molecular similarity based methods for selecting compounds for
screening. In: Rouvray, D.H., (Ed.), Computational Chemical Graph Theory.
Nova Science, New York, pp. 299-316.
Lall, R.S., 1991. Structure activity relationship of organophosphorous insecticides.
Asian J. Chem. 2, 37-42.
Lamanna, C., Bellini, M., Padova, A., Westerberg, G., Maccari, L., 2008.
Straightforward recursive partitioning model for discarding insoluble
compounds in the drug discovery process. J. Med. Chem. 51, 2891-2897.
Langley, J.N., 1905. On the reaction of cells and nerve endings to certain poisons. J.
Physiol. 33, 374-413.
Lasney, M., 2000. Patents and Potions: Entering the Pharmaceutical Century, The
Pharmaceutical Century: Ten decades of Drug Discovery Supplements to
American Chemical Society, pp. 18-31.
Lather, V., Madan, A.K, 2005a. Application of graph theory: topological models for
prediction of CDK-1 inhibitory activity of aloisines. Croat. Chem. Acta. 78, 55-
61.
Lather, V., Madan, A.K, 2005b. Topological models for the prediction of HIV-
protease inhibitory activity of tetrahydropyrimidin-2-ones. J. Mol. Graph.
Model. 23, 339-345.
Lather, V., Madan, A.K., 2004. Models for the prediction of adenosine receptors
binding activity of 4-amino[1,2,4]triazolo[4,3-a]quinoxalines. J. Mol. Struct.
(THEOCHEM). 678, 1-9.
Leach, A.R., Bryce, R.A., Robinson, A.J., 2000. Synergy between combinatorial
chemistry and de novo design. J. Mol. Graph. Model. 18, 358-367.
Lemmen, C., Lengauer, T., 2000. Computational methods for the structural alignment
of molecules. Journal of Computer-Aided Molecular Design. 14 (3), 215-232.
Leow, J.L., Baron, R., Casey, P.J., Go, M., 2007. Quantitative structure-activity
relationship (QSAR) of indoloacetamides as inhibitors of human
isoprenylcysteine carboxyl methyl transferases. Bioorg. Med. Chem. Lett.,
17(4), 1025-1032.
Lewis, R.A., 2005. A general method for exploiting QSAR models in lead
optimization. J. Med. Chem. 48, 1638-1648.
References
307
Li, J., Gramatica, P., 2010. Classification and virtual screening of androgen receptor
antagonists. J. Chem. Inf. Model. 50, 861-874.
Li, X., Yu, Q., Zhu, L., 2000. A novel quantum-topology index. J. Chem. Inf.
Comput. Sci. 40, 399-402.
Lill, M.A., 2007. Multi-dimensional QSAR in drug discovery. Drug Discov. Today.
12, 1013-1017.
Linert, W., Lukovits, I., 1997. Formula for the Hyper-Wiener and Hyper detour indices
of fused bicyclic structures. Comm. Math. Chem. (MATCH). 35, 65-74.
Linnan, H.E., Jurs, P.C., Kreatsoulas, C., Custer, L.L., Durham, S.K., Pearl, G.M.,
2005. Probabilistic neural network multiple classifier system for predicting the
genotoxicity of quinolone and quinoline derivatives. Chem. Res. Toxicol. 18,
428-440.
Liu, H.X., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D., Fan, B.T., 2004. QSAR and
classification models of a novel series of COX-2 selective inhibitors: 1, 5-
diarylimidazoles based on support vector machines. J. Comput. Aided Mol. Des.
18, 389–399.
Liu, P., Agrafiotis, D.M., Rassokhin, D.N., 2011. Power keys: a novel class of
topological descriptors based on exhaustive subgraph enumeration and their
application in substructure searching. J. Chem. Inf. Model. 51, 2843-2851.
Liu, W., Johnson, D.E., 2009. Clustering and its application in multi-target prediction.
Curr. Opin. Drug Discov. Develop. 12, 98-107.
Lohninger, H., 1993. Evaluation of neural networks based on radial basis functions and
their application to the prediction of boiling points from structural parameters. J.
Chem. Inf. Comput. Sci. 33, 736-744.
Lovasz, L., Pelikan, J., 1973. On the eigenvalue of trees. Period. Math. Hung. 3, 175-
182.
Lu, C., Guo, W., Hu, X., Wang, Y., Yin, C., 2006. A Lu index for QSAR/QSPR
studies. Chem. Phy. Lett. 417, 11-15.
Lukovits, I. Linert, W., 2001. A topological account of chirality. J. Chem. Inf.
Comput. Sci. 41, 1517-1520.
Lukovits, I., 1996. The Detour index. Croat. Chem. Acta. 69, 873-882.
References
308
Lukovits, I., 1998. An all-path vesrion of the Wiener index. J. Chem. Inf. Comput. Sci.
38, 125-129.
Lukovits, I., 2000. A compact form of the adjacency matrix. J. Chem. Inf. Comput.
Sci. 40, 1147-1150.
Lukovits, I., Linert, W., 1994. A novel definition of the hyper-Wiener index for
cycles. J. Chem. Inf. Comput. Sci. 34, 899-902.
Ma, H., Gao, Y., Li, X., Ma, J., Liu, C., Jiang, Y., 2003. Edge structure index for
evaluating the ground state properties of one-dimensional macro-to
suprabenzenoid hydrocarbons as examined by DMRG VB calculations. Internet
Electron. J. Mol. Des. 2, 209-223.
Madan A.K., Dureja H., 2010. Eccentricity Based Descriptors for QSAR/QSPR, in:
Novel molecular structure descriptors—theory and applications II, Gutman I.,
Furtula B., eds.Kragujevac: Serbia, 91–138.
Magnuson, V.R., Harriss, D.K., Basak, S.C., 1983. Topological indices based on
neighborhoood symmetry. In: King, R.B., (Ed.), Chemical Applications of
Topology and Graph Theory. Elsevier Amsterdam, pp 178-191.
Mahmiani, A., Khormali, O., Iranmanesh, A., Yousefidaz, M., 2010. The new version
of Szeged index. Optoelectron. Adv. Mater. Rapid Commun. 4, 2182-2184.
Mahmoudi, N., de Julian-Oritiz, J.V., Ciceron, L., Gálvez, J., Mazier, D., Danis, M.,
Derouin, F., García-Domenech, R., 2006. Identification of new anti-malarial
drugs by linear discriminant analysis and topological virtual screening. J.
Antimicrob. Chemother. 57, 489-497.
Mann, J., 1994. Murder, Magic and Medicine. Oxford University Press, New York, pp.
164-170.
Maren, T.H., 1967. Carbonic anhydrase: Chemistry, physiology, and inhibition.
Physiol. Rev. 47, 595-781.
Martin, Y.C., Holland, J.B., Jarboe, C.H., Plaotnikov, N., 1974. Discriminant analysis
of the relationship between physical properties and the inhibition of monoamine
oxidase by aminotetrelins and aminoindans. J. Med. Chem. 17, 409-413.
Martis, E.A., Radhakrishnan R., Badve, R.R., 2011. High throughput screening: the
hits and leads of drug discovery- an overview. J. Appl. Pharm. Sci. 1, 2-10.
Marwaha R.K., Jangra H., Das K.C., Bharatam P.V., Madan A.K., 2012. Fourth
generation detour matrix based topological indices for QSAR/QSPR: Part-1:
References
309
development and evaluation. Int. J. Computational Biology and Drug Design.
5(3/4), 335-360.
Maryanoff, B.E., 2009. Drug discovery and medicinal chemist. Future Med. Chem. 1,
11-15.
Masai, H., Arai, K., 2002. Cdc7 kinase complex: a key regulator in the initiation of
DNA replication. J. Cell Physiol. 190, 287–296.
Masai, H., Sato, N., Takeda, T., Arai, K., 1999. CDC7 kinase complex as a molecular
switch for DNA replication. Front Biosci. 4, 834-840.
Massarelli, I., Imbriani, M., Coi, A., Saraceno, M., Carli, N., Bianucci, A.M., 2009.
Development of QSAR models for predicting hepatocarcinogenic toxicity of
chemicals. Eur. J. Med. Chem. 44, 3658-3664.
Massarelli, I., Imbriani, M., James, T.L., Mundula, T., Bianucci, A.M., 2011.
Development of classification model batteries for predicting inhibition of
tubulin polymerization by small molecules. Chemomet. Intell. Lab. 107, 206-
214.
Matthews, B.W., 1975. Comparision of the predicted and observed secondary
structure of T4 phase lysozyme. Biochim. Biophys. Acta. 405, 442-451.
May, M., 2012. Expanding the reach of lead identification. Drug Discov. Develop.
Mag. Available at: www.dddmag.com. (Accessed on 16/07/2014).
McCulloch, W., Pitts, W., 1943. A logical calculus of ideas immanent in nervous
activity. Bull. Math. Biophy. 5, 115-133.
McGee, P., 2005. Modeling success with in silico tools. Drug Discov Today. 8, 23–
28.
Mekenyan, O., Bonchev, D., 1986. OASIS method for predicting biological activity
of chemical compounds. Acta. Pharm. Yugoel. 36, 225-237.
Menichincheri, M., Albanese, C., Alli, C., Ballinari, D., Bargiotti, A., Caldarelli, M.,
Ciavolella, A., Cirla, A., Colombo, M., Colotta, F., Croci, V., D'Alessio, R.,
D'Anello, M., Ermoli, A., Fiorentini, F., Forte, B., Galvani, A., Giordano, P.,
Isacchi, A., Martina, K., Molinari, A., Moll, J.K., Montagnoli, A., Orsini, P.,
Orzi, F., Pesenti, E., Pillan, A., Roletto, F., Scolaro, A., Tatò, M., Tibolla, M.,
Valsasina, B., Varasi, M., Vianello, P., Volpi, D., Santocanale, C., Vanotti, E.,
2010. Cdc7 kinase inhibitors: 5-heteroaryl-3-carboxamido-2-aryl pyrroles as
potential antitumor agents. 1. Lead finding. J. Med. Chem. 53(20), 7296-315.
References
310
Mercader, A., Castro, E.A., Toropov, A.A., 2001. Maximum topological distances
based indices as molecular descriptors for QSPR. 4. modeling the enthalpy of
formation of hydrocarbons from elements. Int. J. Mol. Sci. 2, 121-132.
Meyer, H., 1899. On the theory of alcohol narcosis I. Which property of anesthetics
gives them their narcotic activity? Arch. Exp. Pathol. Pharmakol. 42, 109-118.
Michielan, L., Moro, S., 2010. Pharmaceutical perspectives of nonlinear QSAR
strategies. J. Chem. Inf. Model. 50, 961-978.
Milicevci, A., Nikolic, S., Plavsic, D., Trinajstic, N., 2003. On the Hosoya Z index of
general graphs. Internet Electron. J. Mol. Des. 2, 160-178.
Mills, E.J., 1884. On melting point and boiling point as related to composition.
Philos. Mag. 17, 173-187.
Milne, G.W.A., 1997. Mathematics as a basic of chemistry. J. Chem. Inf. Comput. Sci.
37, 639-644.
Mitscher, L.A., 2002. Drug design and discovery: an overview. In: Textbook of drug
design and discovery. Krogsgaard-Larsen, P., Liljefors, T., Madsen, U., (Eds.),
3rd
edition, Taylor and Francis, London, pp.1-34.
Modi, S., Hughes, M., Garrow, A., White, A., 2012. The value of in silico chemistry
in the safety assessment of chemicals in the consumer goods and pharmaceutical
industries. Drug Discov. Today. 17, 135-142.
Moghani, G.A., Ashrafi, A.R., 2006. On the PI index of some nanotubes. J. Phy.
Conference Series. 29, 159-162.
Mohar, B., 1989. Laplacian matrices of graphs. Stud. Phys. Theor. Chem. 63, 1-8.
Moradi, S., Baba-Rahim, S., 2013. The eccentric connectivity index of V-phenylenic
nanotubes and nanotorus. Fuller. Nanotu. Car. Nanostruct. 21, 326-332.
Moreau, G., Broto, P., 1980a. Autocorrelation of molecular structures: application to
SAR studies. Nouv. J. Chim. 4, 757-764.
Moreau, G., Broto, P., 1980b. The autocorrelation of a topological structure: a new
molecular descriptor. Nouv. J. Chim. 4, 359-360.
Morgan, H.L., 1965. The generation of a unique machine description for chemical
structures-a technique developed at chemical abstract service. J. Chem. Doc. 5,
107-113.
References
311
Mosier, P.D., Jurs, P.C., Custer, L.L., Durham, S.K., Pearl, G.M., 2003. Predicting the
genotoxicity of thiophene derivatives from molecular structure. Chem. Res.
Toxicol. 16, 721-732
Motoc, I., Marshall, G.R., Dammkoehler, R.A., Labanowski, J.Z., 1985. Molecular
shape descriptors three dimensional molecular shape discriptors. Zeitschrift
Naturforschung Teil. 40, 1108-1113.
Mowshowitz, A., 1968a. Entropy and the complexity of graphs. I. an index of the
relative complexity of a graph. Bull. Math. Biophys. 30, 175-204.
Mowshowitz, A., 1968b. Entropy and the complexity of graphs. III. graphs with
prescribed information content. Bull. Math. Biophys. 30, 387-414.
Mu, L., Feng, C., He, H., 2008. Modeling diamagnetic susceptibilities of organic
compounds with a novel connectivity index. Ind. Eng. Chem. Res. 47, 2428-
2433.
Mu, L., He, H., Yang, W., Feng, C., 2009. Variable molecular connectivity indices for
predicting the diamagnetic susceptibilities of organic compounds. Ind. Eng.
Chem. Res. 48, 4165-4175.
Nadine, S., Christine, J., Claudia, A., Michael, C.H., 2008. Gradual in-silico filtering
for drug like substances. J. Chem. Inf. Model. 48, 613–628
Narumi, H., 2003. Statisco-mechanical aspect of the Hosoya index. Internet Electron.
J. Mol. Des. 2, 375-382.
Narumi, H., Katayama, M., 1984. Simple topological index. a newly devised index
characterizing the topological nature of structural isomers of saturated
hydrocarbons. Mem. Fac. Engin. Hokkaido Univ. 16, 209-214.
Natarajan, R., 2011. New topological indices with very high discriminatory power.
SAR QSAR Environ. Res. 22, 1-20.
Natarajan, R., Nirdosh, I., 2003. Application of topochemical, topostructural,
physicochemical and geometrical parameters to model the flotation efficiencies
of N-arylhydroxamic acid. Int. J. Miner. Process. 71, 113-129.
Neter, J., Wasserman, W., Whitmore, GA., 1988. Applied Statistics, 3rd
ed. Allyn and
Bacon Inc., Boston, Massachusetts.
Nie, C., Wu, Y., Wu, R., Wen, S., 2012. An improved topological descriptor ˊEDm
and its application. J. Chilean Chem. Soc. 57, 955-963.
References
312
Nikolic, S., Kovacevic, G., Milicevic, A., Trinajstic, N., 2003. The Zagreb indices 30
years after. Croat. Chem. Acta. 76, 113-124.
Nikolic, S., Tolic, I.M., Trinajstic, N., Baucic, I., 2000. On the Zagreb indices as
complexity indices. Croat. Chem. Acta. 73, 909-921.
Noureen, N., Rashid, H., Kalsoom, S., 2010. Identification of type-specific anticancer
histone deacetylase inhibitors: Road to success. Cancer Chemo. Pharmacol. 66,
625-633.
O’Brien, S.E., Popelier, P.L., 2001. Quantum molecular similarity. 3. QTMS
descriptors. J. Chem. Inf. Comput. Sci. 41, 764-775.
Ortiz, A.R., Pisabarro, A.T., Federico, G., Wade, J., 1995. Prediction of drug binding
affinities by comparative binding energy analysis. J. Med. Chem. 38, 2681-
2691.
Pal, D.K., Sengupta, C., De, A.U., 1988. A new topochemical descriptor TAU in
molecular connectivity concept: Part 1-aliphatic compounds. Ind. J. Chem. 27B,
734-739.
Pal, D.K., Sengupta, C., De, A.U., 1989. Introduction of a novel topochemical index
and exploitation of of group connectivity concept to achieve predictability in
QSAR and RDD. Ind. J. Chem. 28B, 261-267.
Palyulin, V.A., Radchenko, E.V., Zefirov, N.S., 2000. Molecular field topology
analysis method in QSAR studies of organic compounds. J. Chem. Inf. Comput.
Sci. 40, 659-667.
Parascandola, J., 2003. To bond or not to bond: chemical versus physical theories of
drug action. Bull. Hist. Chem. 28, 1-8.
Pearlman, R.S., Smith, K.M., 1998. Novel software tools for chemical diversity. In:
Kubinyi, H., Martin, Y., Folkers, G., (Eds.), 3D-QSAR and drug design: recent
advances. Kluwer Academic Publishers, Dordrecht, Netherlands, pp. 339-353.
Pearlman, R.S., Smith, K.M., 1999. Metric validation and the receptor-relevant
subspace concept. J. Chem. Inf. Comput. Sci. 39, 28-35.
Peltason, L., Bajorath, J., 2007. SAR index: quantifying the nature of structure
activity relationships. J. Med. Chem. 50, 5571-5578.
Petitjean, M., 1992. Applications of the radius-diameter diagram to the classification
of topological and geometrical shapes of chemical compounds. J. Chem. Inf.
Comput. Sci. 32, 331-337
References
313
Phatak, A., De Jong S., 1997. The geometry of partial least squares. J. Chemom. 11,
311-338.
Pizzi, R., 2000. Salving with Science: The roaring twenties and the great depression,
The Pharmaceutical Century: Ten decades of Drug Discovery. Supplements to
American Chemical Society, pp. 34-51.
Platt, J.R., 1947. Influence of neighbor bonds on additive bond properties in paraffins.
J. Chem. Phys. 15, 419-20.
Plavsic, D., Nikolic, S., Trinajstic, N., Nihalic, Z., 1993. On the Harary index for the
characterization of chemical graphs. J. Math. Chem. 12, 235-250.
Plavsic, D., Soskic, M., Dakovic, Z., Gutman, I., Graovac, A., 1997. Extension of the
Z matrix to cycle-containing and edge-weighted molecular graphs. J. Chem. Inf.
Comput. Sci. 37, 529-534.
Plavsic, D., Trinajstic, N., Amic, D., Soskic, M., 1998. Comparison between the
structure-boiling point relationship with different descriptors for condensed
benzenoids. New J. Chem. 22, 1075-1077.
Pogliani, L., 2003. Graph-theoretical concepts and physiochemical data. Data Sci. J.
2, 1-11.
Pompe, M., 2005. Variable connectivity index as a tool for solving the ‘anti-
connectivity’ problem. Chem. Phy. Lett. 404, 296-299.
Pompe, M., Randic, M., 2006. Anti connectivity: a challenge for structure-property-
activity studies. J. Chem. Inf. Model. 46, 2-8.
Prasad, A.M., Iverson, L.R., Liaw, A., 2006. Newer classification and regression tree
techniques: Bagging and Random Forests for ecological prediction. Ecosystems.
9, 181-199.
Prentis, R.A., Lis, Y., Walker, S.R., 1988. Pharmaceutical innovation by the seven
UK-owned pharmaceutical companies (1964-1985). Br. J. Clin. Pharmacol. 25,
387-396.
Qi, Y., 2012. Random forest for bioinformatics. In: Zhang, C., Ma, Y., (Eds.,),
Ensemble Machine Learning: Methods and Applications, Springer-Verlag New
York.
Qiao, C., Gupte, A., Boshof, H.I., Wilson, D.J., Bennett, E.M., Somu, R.V., Barry,
C.E., Aldrich, C.C., 2007. 5-O-[(N-Acyl)sulfamoyl]adenosines as
References
314
antitubercular agents that inhibit MbtA: An adenylation enzyme required for
sderophore biosynthesis of the mycobactins. J. Med. Chem. 50, 6080-6094.
Quigley, J.M., Naughton, S.M., 2002. The interrelation of physicochemical
parameters and topological descriptors for a series of β-blocking agents. J.
Chem. Inf. Comput. Sci. 42, 976-982.
Quinlan, J.R., 1986. Induction of decision trees. Machine Learning. 1, 81-106.
Rabal, O., Oyarzabal, J., 2012. Using novel descriptor accounting for ligand-receptor
interactions to define and visually explore biologically relevant chemical space.
J. Chem. Inf. Model. 52, 1086-1102.
Randic, M., 1973. On the recognition of identical graphs representing molecular
topology. J. Chem. Phys. 60, 3920-3928.
Randic, M., 1974. On the recognition of identical graphs representing molecular
topology. J. Chim. Phys. 60, 3920-3928.
Randic, M., 1975. On characterization of molecular branching. J. Am. Chem. Soc. 97,
6609-6615.
Randic, M., 1980. Random walks and their diagnostic value for characterization of
atomic environment. J. Comput. Chem. 4, 386-399.
Randic, M., 1984. On molecular identification numbers. J. Chem. Inf. Comput. Sci.
24, 164-175.
Randic, M., 1991a. Generalized molecular descriptors. J. Math. Chem. 7, 155-168.
Randic, M., 1991b. Orthogonal molecular descriptors. New J. Chem. 15, 517-525.
Randic, M., 1992. Representation of molecular graphs by basic graphs. J. Chem. Inf.
Comput. Sci. 32, 57-69.
Randic, M., 1993. Novel Molecular descriptors for structure-property studies. Chem.
Phys. Lett. 211, 478-483.
Randic, M., 1994b. Hosoya matrix-a source of new molecular descriptors. Croat.
Chem. Acta 34, 368-376.
Randic, M., 1995. Molecular profiles - Novel geometry-dependent molecular
descriptors.New J.Chem. 19, 781-791.
Randic, M., 1995. Molecular Shape Profiles. J. Chem. Inf. Comp. Sci. 35, 373-382.
References
315
Randic, M., 1997a. Linear combination of path numbers as molecular descriptors.
New J. Chem. 21, 945-951.
Randic, M., 1997b. On characterization of cyclic structures. J. Chem. Inf. Comput.
Sci. 37, 1063-1071.
Randic, M., 2001a. Novel shape descriptors for molecular graphs. J. Chem. Inf.
Comput. Sci. 41, 607-613.
Randic, M., 2001b. Graph valence shells as molecular descriptors. J. Chem. Inf.
Comput. Sci. 41, 627-630.
Randic, M., 2004. Wiener-Hosoya index-a novel graph theoretical molecular
descriptor. J. Chem. Inf. Comput. Sci. 44, 373-377.
Randic, M., Dobrowolski, J. Cz., 1998. Optimal molecular connectivity descriptors
for nitrogen containing molecules. Int. J. Quant. Chem. 70, 1209-1215.
Randic, M., Guo, X., Oxley, T., Krishnapriyan, H., Naylor, L., 1994a. Wiener matrix
invariants. J. Chem. Inf. Comput. Sci. 34, 361-367.
Randic, M., Pisanski, T., Novic M., Plavsic, D., 2010. Novel graph distance matrix. J.
Comput. Chem. 31, 1832-1841.
Randic, M., Plavsic, D., Lers, N., 2001a. Variable connectivity index for cycle-
containing structures. J. Chern. Inf. Comput. Sci. 41, 657-662.
Randic, M., Pompe, M., 2001. The variable molecular descriptors based on distance
related matrices. J. Chem. Inf. Comput. Sci. 41, 575-581.
Randic, M., Razinger, M., 1995. Molecular topographic indices. J. Chem. Inf.
Comput. Sci. 35, 140-147.
Randic, M., Trinajstic, N., 1994. Notes on some less known early contributions to
chemical graph theory. Croat. Chem. Acta. 67, 1-35.
Randic, M., Zupan, J., 2001. On interpretation of well-known topological indices. J.
Chem. Inf. Comput. Sci. 41, 550-560.
Rao, V.S., Srinivas, K., 2011. Modern drug discovery process: An in silico approach.
J. Bioinform. Seq. Anal. 2, 89-94.
Rashevsky, N., 1955. Life, information theory and topology. Bull. Math. Biophys. 17,
229-235.
References
316
Ratti, E., Trist, D., 2001. Continuing evolution of the drug discovery process in the
pharmaceutical industry. Pure Appl. Chem. 73, 67-75.
Ravina, E., 2011. Evolution of drug discovery: from traditional medicines to modern
drugs, Wiley-VCH, Weinheim.
Raychadhury, C., Ray, S.K., Ghosh, J.J., Roy, A.B., Basak, S.C., 1984.
Discrimination of isomeric structures using information theoretic topological
indices. J. Comput. Chem. 5, 581-588.
Raychaudhury, C., 1983. Ph.D. Thesis, Jadavpur University, Calcutta, India, 1983.
Raychaudhury, C., Ghosh, I., 2004. Information-theoretical measure of similarity and
a topological shape and size descriptor for molecular similarity analysis.
Internet Electron. J. Mol. Des. 3, 350-360.
Razinger, M., 1982. Extended connectivity in chemical graphs. Theor. Chim. Acta. 61,
581-586.
Razinger, M., 1986. Discrimination and ordering of chemical structures by the Number
of Walks. Theor. Chim. Acta. 70, 365-378.
Reddy, A.S., Pati, S.P., Kumar, P.P., Pradeep, H.N., Sastry, G.N., 2007. Virtual
screening in drug discovery - A computational perspective. Curr. Protein Pept.
Sci. 8, 329-351.
Reid, T.S., Terry, K.L., Casey, P.J., Beese, L.S., 2004. Crystallographic analysis of
CaaXprenyltransferases complexed with substrates defines rules of protein
substrate selectivity. J. Mol. Biol. 343, 417–433.
Ren, B., 1999. A new topological index for QSPR of alkanes. J. Chem. Inf. Comput.
Sci. 39, 139-143.
Ren, B., 2002a. Novel atomic level based AI topological descriptors: application to
QSPR/QSAR modeling. J. Chem. Inf. Comput. Sci. 42, 858-868.
Ren, B., 2002b. Novel atom-type AI indices for QSPR studies of alcohols. Comput.
Chem. 26, 223-235.
Richardson, B.J., 1868. Physiological research on alcohols. Med. Times Gaz. 2, 703-
706.
Richet, C., 1893. On the relationship between the toxicity and the physical properties
of substances. Compt. Rendus Seances Soc Biol. 9, 775-776.
References
317
Rohrbaugh, R.H., Jurs, P.C., 1987. Descriptions of molecular shape applied in studies
of structure/activity and structure/property relationships. Anal. Chim. Acta. 199,
99-109.
Romano, J.D., Schmidt, W.K., Michaelis, S., 1998. The Saccharomyces cerevisiae
prenylcysteine carboxyl methyltransferase Ste14p is in the endoplasmic
reticulum membrane, Mol Biol. Cell. 9, 2231–2247.
Rourvray, D.H., 1991. The origins of chemical graph theory. In: Bonchev, D.,
Rouvray, D.H., (Eds.), Chemical graph theory: Introduction and fundamentals.
Abacus Press, New York, pp. 1-34.
Rouvray, D.H., 1973. The search for useful topological indices in chemistry. Amer.
Sci. 61(6), 729-735.
Rouvray, D.H., 1976. On the sum of distance matrix elements, In: Balaban, A.T.,
(Ed.) Chemical Applications of Graph Theory, Academic Press, New York.
Rowland, J. L., Niederweis, M., 2012. Resistance mechanisms of Mycobacterium
tuberculosis against phagosomal copper overload. Tuberculosis. 92(3), 202-210.
Roy, K., 2004. Topological descriptors in drug design and modeling studies. Mol.
Divers. 8, 321-323.
Roy, K., Das, R.N., 2011. On Extended Topochemical Atom (ETA) Indices for QSPR
Studies’, in: Castro, E.A. Hagi, A.K (Eds.): Advanced Methods and
Applications in Chemoinformatics: Research Progress and New Applications,
IGI Global, PA, pp.380–411.
Roy, K., Ghosh, G., 2003. Introduction of extended topological atom (ETA) indices in
the valence electron mobile (VEM) environment as tools for QSAR/QSPR
studies. Internet Electron. J. Mol. Des. 2(9), 599–620.
Roy, K., Ghosh, G., 2004. QSTR with extended topochemical atom indices. 3.
toxicity of nitrobenzenes to tetrahymena pyriformis. QSAR Comb. Sci. 23, 99-
108.
Roy, K., Ghosh, G., 2010. Exploring QSARs with Extended Topochemical Atom
(ETA) indices for modeling chemical and drug toxicity. Current
Pharmaceutical Design. 16(24), 2625–2639.
Roy, K., Mitra, I., 2012. Electrotopological state atom E-state index in drug design,
QSAR, property prediction and toxicity assessment. Curr. Comput. Aided Drug
Des. 8, 135-158.
References
318
Rucker, G., Rucker, C., 1993. Counts of all walks as atomic and molecular descriptors.
J. Chem. Inf. Comput. Sci. 33, 683-695.
Sabljic, A., Trinajstic, N., 1981. Quantitative structure activity relationship: role of
topological indices. Acta Pharm. Jugosl. 31, 198-214.
Saha S., Bandyopadhyay, S., 2012. Some connectivity based cluster validity indices.
Appl. Soft Comput. 12, 1555-1565.
Sahu, P.K., Lee, S.L., 2004. Novel information theoretic topological index Ik for
unsaturated hydrocarbons. Chem. Phy. Lett. 396, 465-468.
Sahu, P.K., Lee, S.L., 2008. Net-sign identity information index: a novel approach
towards numerical characterization of chemical signed graph theory. Chem.
Phys. Lett., 454, 133–138.
Sardana, S., Madan, A.K., 2001. Application of graph theory: relationship of
molecular connectivity index, Wiener’s index and eccentric connectivity index
with diuretic activity. MATCH Commun. Math. Comput. Chem. 43, 85-98.
Sardana, S., Madan, A.K., 2002a. Predicting anticonvulsant activity of
benzamides/benzylamines: computational approach using topological
descriptors. J. Comput. Aided Mol. Des. 16, 545-550.
Sardana, S., Madan, A.K., 2002b. Predicting anti-HIV activity of TIBO derivatives: A
computational approach using a novel topological descriptor. J. Mol. Model. 8,
258-265.
Schneider, G., 2000. Neural networks are useful tools for drug design. Neural Netw.
13, 15-16.
Schultz, H.P., 1989. Topological organic chemistry. 1. graph theory and topological
indices of alkanes. J. Chem. Inf. Comput. Sci. 29, 227-228.
Schuur, J., Selzer, P., Gasteiger, J., 1996. The coding of the three-dimensional
structure of molecules by molecular transforms and its application to structure–
spectra correlations and studies of biological activity. J. Chem. Inf. Comput. Sci.
36, 334-344.
Sclafani, R. A., 2000. Cdc7p‐Dbf4p becomes famous in the cell cycle. J. Cell Sci.
113, 2111–2117.
Selassie, C.D., 2003. History of quantitative structure-activity relationships. In:
Abraham D.J., (Ed.), Burger's Medicinal Chemistry and Drug Discovery. 6th
ed.,
Vol 1, Wiley & Sons, Inc. pp 1-48.
References
319
Seufferlein, T., Ahn, J., Krndija, D., Lother, U., Adler, G., Wichert G.V., 2009.
Tumour biology and cancer therapy – an evolving relationship. Cell Comm.
Signal. 7(19), 1-10.
Shamsipur, M., Ghavami R., Hemmateenejad, B., Sharghi, H., 2004b. Highly
correlating distance/connectivity–based topological indices. 2: prediction of 15
properties of a large set of alkanes using a stepwise factor selection-based PCR
analysis. QSAR Comb. Sci. 23, 734-753.
Shamsipur, M., Hemmateenejad, B., Akhond, M., 2004a. Highly correlating
distance/connectivity–based topological indices. 1: QSPR studies of alkanes.
Bull. Korean Chem. Soc. 25, 1-7.
Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27,
379-423.
Shannon, C.E., Weaver, W., 1949. The mathematical theory of communication.
University of Illinois Press, Urbana.
Sharma, V., Goswami, R., Madan, A.K., 1997. Eccentric connectivity index: a novel
highly discriminating topological descriptor for structure property and structure
activity studies. J. Chem. Inf. Comput. Sci. 37, 273-282.
Shen, C.F., Kimberly, Mason, K.A., Katz, A.H., 2012. The use of cluster analysis in
molecular design. Available at: http://www.sascommunity.org.
Singh M., Wadhwa P.K., Kaur S., Predicting protein function using decision
tree.World Acad. Sci. Eng. Technol., 2008, 39, 350–353.
Singh, M., Dureja, H., Madan, A.K., 2014. Detour matrix based adjacent path
eccentric distance sum indices for (Q)SAR/QSPR Part II: Application in
development of models for COX-2 inhibitory activity of indomethacin
derivatives. Int. J. Comput. Bio. Drug Des. In press.
Singh, M., Gupta, S., Das, K.C., Madan, A.K., 2014. Improved Variable Zagreb
topochemical indices: Highly discriminating topological descriptors for
QSAR/QSPR modeling, Int. J. Chemical Modeling. In press.
Singh, M., Gupta, S., Lather, V., Madan, A. K., 2013. Development of models for
CB2 cannabinoid receptor agonist activity using refined general Randic indices,
Int. J. Chem. Model. 5(4), 419-455.
Singh, M., Jangra, H., Bharatam P.V. and Madan A.K., 2014. Detour matrix based
adjacent path eccentric distance sum indices for (Q)SAR/QSPR PART I:
Development and Evaluation, Int. J. Comput. Bio. Drug Des. In press
References
320
Sippl, W., 2000. Receptor-based 3DQSAR analysis of estrogen receptor ligands-
merging the accuracy of receptor-based alignments with the computational
efficiency of ligand-based methods. J Comput Aided Mol Des. 14:559–572.
Skorobogatov, V.A., Konstantinova, E.V., Nekrasov, Y.S., Sukharev, Y.N., Tepfer,
E.E., 1991. On the correlation between the molecular information topological
and mass-spectra indices of organometallic compounds. MATCH Commun.
Math. Comput. Chem. 26, 215-228.
Sneader, W. 1985. Drug Discovery: The Evolution of Modern Medicines. John Wiley
& Sons, Chichester.
Sneader, W. 1996. Drug prototypes and their exploitation, John Wiley & Sons,
Chichester.
Somberg, J.C., 1996. The evolving drug discovery process. In: Welling, P.G.,
Lasagna, L., Banakar, U.V. (Eds.), The Drug Discovery Process: Increasing
Efficiency and Cost effectiveness. Marcel Dekker, New York.
Spialter, L., 1963. The atom connectivity matrix ACM and its characteristic
polynomial ACMCP: a new computer-oriented chemical nomenclature. J. Am.
Chem. Soc. 85, 2012-2013.
Spialter, L., 1964. The atom connectivity matrix ACM and its characteristic
polynomial ACMCP. J. Am. Chem. Soc. 86, 261-269.
Srivani, P., Usharani, D., Jemmis, E.D., Sastry, G. N., 2008. Subtype selectivity in
phosphodiesterase 4 (PDE4): a bottleneck in rational drug design. Curr. Pharm.
Des. 14(36), 3854–3872.
Srivastava, H.K., Pasha, F.A., Mishra, S.K., Singh, P.P., 2009. Novel applications of
atomic softness and QSAR study of testosterone derivatives. Med.chem. Res.,
18(6), 455–466.
Stanton, D.T., 1999. Evaluation and use of BCUT descriptors in QSAR and QSPR
studies. J. Chem. Inf. Comput. Sci. 39, 11-20.
Stanton, D.T., 2008. On the importance of topological descriptors in understanding
structure-property relationships. J. Comput. Aided Mol. Des. 22, 441-460.
Stanton, D.T., Jurs, P.C., 1990. Development and use of charged partial surface area
structural descriptors in computer-assisted quantitative structure–property
relationship studies. Anal. Chem. 62, 2323-2329.
References
321
Sternweis, P.C., Gilman, A.G., 1982. Aluminum: a requirement for activation of the
regulatory component of adenylate cyclase by fluoride. Proc. Natl. Acad. Sci.
USA 79, 4888-4891.
Stoll, A., Hofmann, A., 1943. Partialsynthese von Alkaloiden Typus des Ergobasins.
Helv Chim Acta. 26, 944-947.
Su, H., Heinonen, M., Rousu, J., 2010. Structured output prediction of anti-cancer
drug activity. In: Dijkstra, T.M.H., Tsivtsivadze E., Marchiori, E., Heskes, T.,
(Eds.), Proceedings of the 5th IAPR international conference, pattern
recognition in bioinformatics (PRIB). Springer-Verlag, Berlin, Heidelberg,
pp.38-49.
Suh, M.E., Park, S.Y., 2002. Lee H.J.Comparison of QSAR Methods (CoMFA,
CoMSIA, HQSAR) of anticancer 1-N-Substituted imidazoquinoline-4, 9-dione
Derivatives. Bull. Korean Chem. Soc. 23(3), 417-422.
Sun, H., 2005. A naive bayes classifier for prediction of multidrug resistance reversal
activity on the basis of atom typing. J. Med. Chem. 48, 4031-4039.
Sun, Y., Peng, Y., Chen, Y., Shukla, A.J., 2003. Application of artificial neural
networks in the design of controlled release drug delivery systems. Adv. Drug
Deliver. Rev. 55, 1201-1215.
Sussman, N.L., Kelly, J.H., 2003. Saving time and money in drug discovery- a pre-
emptive approach. Business Briefings: Future Drug Discov. 46-49.
Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P., 2003.
Random forest: a classification and regression tool for compound classification
and QSAR modeling. J. Chem. Inf. Comput. Sci. 43, 1947-1958.
Swords, R., Mahalingam, D., O'Dwyer, M., Santocanale, C., Kelly, K., Carew, J.,
Giles, F., 2010. Cdc7 kinase - a new target for drug development. Eur J Cancer.
46(1), 33-40
Sylvester, J.J., 1878. On an application of the new atomic theory to the graphical
representation of the invariants and covariants of binary quantics, with three
appendices. Am. J. Math. 1, 64-90.
Szymanski, K., Muller, W.R., Knop, J.V., Trinajstic, N., 1985. On Randic’s molecular
identification numbers. J. Chem. Inf. Comput. Sci. 25, 413-415.
Taft, R.W., 1956. Steric effects in organic chemistry, Wiley, New York. pp. 540-556.
References
322
Talevi, A., Bellera, C.L., Castro, E.A., Bruno-Blanch, L.E., 2007. A successful virtual
screening application: prediction of anticonvulsant activity in MES test of widely
used pharmaceutical and food preservatives methylparaben and propylparaben. J.
Comput. Aided Mol. Des. 21, 527–538.
Tang, B.K., Kalow, W., 1995. Variable activation of lovastatin by hydrolytic enzymes
in human plasma and liver. J. Clin. Pharmacol. 47, 449-451.
Terstappen, G.C., Reggiani, A., 2001. In silico research in drug discovery. Trends
Pharmacol. Sci. 22, 23-26.
Tetko, I.V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P.,
Palyulin, V.A., Radchenko, E.V., Zefirov, N.S., Makarenko, A.S., Tanchuk,
V.Y., Prokopenko, V.V., 2005. Virtual computational chemistry laboratory -
design and description. J. Comput. Aided Mol. Des. 19, 453-463.
Thomas, K.D., Adhikari, A.V., Chowdhury, I.H., Sandeep, T., Mahmood, R.,
Bhattacharya, B., Sumwsh, E., 2011. Design, synthesis and docking studies of
quinoline-oxazolidinone hybrid molecules and their antitubercular properties.
Eur. J. Med. Chem. 46, 4834-4845.
Timar, J., Csuka, O., Orosz, Z., Jeney, A., Kopper, L., 2001. Molecular pathology of
tumor metastasis. I. Predictive pathology. Pathol. Oncol. Res. 7, 217–230.
Todeschini, R., 2000. Molecular descriptors and chemometrics: a powerful combined
tool for pharmaceutical, toxicological and environment problems. Available at:
http://www.moleculardescriptors.eu. (Accessed on 15/04/2014).
Todeschini, R., Consonni, V., 2000. Handbook of molecular Descriptors: methods
and principal in medicinal chemistry. Vol. 11. Wiley VCH Weuinheim,
Germany.
Todeschini, R., Consonni, V., 2009. Molecular descriptors for chemoinformatics.
Wiley VCH, Weinheim.
Todeschini, R., Consonni, V., 2010. New local vertex invariants and molecular
descriptors based on functions of the vertex degrees. MATCH Commun. Math.
Comput. Chem. 64, 359-372.
Todeschini, R., Gramatica, P., 1997a. 3D-modelling and prediction by WHIM
descriptors. Part 5. Theory development and chemical meaning of WHIM
descriptors. Quant. Struct.-Act. Relat. 16, 113–119.
References
323
Todeschini, R., Gramatica, P., 1997b. The whim theory: new 3D molecular
descriptors for Qsar in environmental modeling. SAR & QSAR Environ. Res. 7,
89-115.
Todeschini, R., Gramatica, P., 1998. New 3D Molecular Descriptors: The WHIM
theory and QSAR applications., In: Kubinyi, H., Folkers, G., Martin Y. C.
(Eds.), 3D QSAR in Drug Design. Vol. 2, Kluwer/Escom, Dordrecht, The
Netherlands, pp. 355-380.
Todeschini, R., Gramatica, P., Marengo, E., Provenzani, R., 1995. Weighted Holistic
Invariant Molecular descriptors. Part 2 Theory development and application on
modelling physico-chemical properties of poly aromatic hydrocarbons.
Chemom. Intell. Lab. Syst. 27, 221–229.
Todeschini, R., Lasagni, M., Marengo, E., 1994. New molecular descriptors for 2D
and 3D structures theory: Part 1. J. Chemomet. 8, 263-272.
Tomioka, H., Namba, K., 2006. Development of antituberculous drugs: current status
and future prospects. Kekkaku. 81(12), 753-774
Tomovic, Z., Gutman, I., 2001. Narumi–Katayama index of phenylenes. J. Serb.
Chem. Soc. 66, 243-247.
Tong, J., Liu, S., Zhou, P., Bulan,W., Li, Z., 2008. A novel descriptor of amino acids
and its application in peptide QSAR. J. Theor. Biol. 253, 90-97.
Tong, W., Welsh, W.J., Shi, L., Fang, H., Perkins, R., 2003. Structure activity
relationship approaches and applications. Environ. Toxicol. Chem. 22, 1680-
1695.
Toropov, A.A., Toropova, A.P., 2004. Nearest neighboring code and hydrogen bond
index in labeled hydrogen-filled graph and graph of atomic orbitals: application
to model of normal boiling points of haloalkanes. J. Mol. Struct. (Theochem).
711, 173-183.
Torrens, F., 2002. Computing the permanent of the adjacency matrix for fullerenes.
Internet Electron. J. Mol. Des. 1, 351-359.
Trinajstic, N. Nikolic, S. Basak, S.C. and Lukovits, I., 2001. Distance indices and
their hyper- counterparts: intercorrelation and use in the structure-property
modeling’, SAR and QSAR Environ. Res. 12, 31-54.
Trinajstic, N., 1983. Chemical graph theory, CRC Press, Boca Raton, Florida.
References
324
Trinajstic, N., Babic, D., Nikolic, S., Plavšic, D., Amic, D., Mihalic, Z., 1994. The
laplacian matrix in chemistry. J. Chem. Inf. Comput. Sci. 34, 368-376.
Trinajstic, N., Milan, R., Klein, D.J., 1986. On the quantitative structure-activity
relationship in drug research. Acta. Pharm. Jugosl. 36, 267-279.
Trinajstic, N., Nikolic, S., Lucic, B., Amic, D., Mihalic, Z., 1997. The Detour matrix
in chemistry. J. Chem. Inf. Comput. Sci. 37, 631-638.
Trindle, C., 1969. Bond index description of delocalization. J. Am. Chem. Soc. 91,
219-220.
Tropsha, A., Gramatica P., Gombar, V.K., 2003. The importance of being earnest:
validation is the absolute essential for successful application and interpretation
of QSPR models. QSAR Comb. Sci. 22, 69-73.
Trucco, E., 1956. A note on the information content of graphs. Bull. Math. Biophys.
18, 129-135.
Tsinopoulos, C., McCarthy, I., 2002. New product development as a complex system
of decisions. IEEE International Engineering Management Conference. 2, 761-
765.
Tuppurainen, K. 1999. EEVA electronic eigenvalue: a new QSAR/QSPR descriptor
for electronic substituent effects based on molecular orbital energies. SAR QSAR
Environ. Res. 10, 39-46.
Turker, L., 2003a. A novel topological index for coding of alternant systems. Ind. J.
Chem. 42A, 1295-1297.
Turker, L., 2003b. Hosoya indices and a new approach to molecular similarity. Ind. J.
Chem. 42A, 1442-1445.
Turker, L., Gumus, S., Atalar, T., 2010. A DFT study on nitro derivatives of pyridine.
J. Energetic Mater. 28, 139-171.
Turner, J.V., Maddalena D.J., Cutler, D.J., 2004. Pharmacokinetic parameter
prediction from drug structure using artificial neural networks. Int. J. Pharm.
270, 209-219.
Tute, M.S., 2005. History and objective of quantitative drug design, In: Hansch C.,
(Ed.), Comprehensive Medicinal Chemistry: The Rational Design, Mechanistic
Study & Therapeutic Application of Chemical Compounds, 1st edition, Volume
4, Pergamon Press, Oxford, England, pp. 1-31.
References
325
Urruticoechea, A., Alemany, R., Balart. J., Villanueva, A., Viñals, F., Capella, G.,
2010. Recent advances in cancer therapy: an overview. Curr Pharm Des. 16(1),
3-10.
Van de Waterbeemd, H., Carter, R.E., Grassy, G., Kubinyi, H., Martin, Y.C., Tute,
M.S., Willett, P., 1998. Glossary of the terms used in computational drug
design. Ann. Rep. Med. Chem. 33, 397.
Vanotti, E., Amici, R., Bargiotti, A., Berthelsen, J., Bosotti, R., Ciavolella, A., Cirla,
A., Cristiani, C., D’Alessio, R., Forte, B., Isacchi, A., Martina, K.,
Menichincheri, M., Molinari, A., Montagnoli, A., Orsini, P., Pillan, A., Roletto,
F., Scolaro, A., Tibolla, M., Valsasina, B., Varasi, M., Volpi, D., Santocanale
C., 2008. Cdc7 Kinase Inhibitors: Pyrrolopyridinones as Potential Antitumor
Agents. 1. Synthesis and Structure–Activity Relationships. J. Med. Chem. 51,
487–501.
Vapnik, V.N., 1998. Statistical Learning Theory. Adaptive and learning systems for
signal processing, communications and control, Wiley, New York.
Varmuza, K., Dehmer, M., Borgert, S., 2009. Molecular descriptors based on entropy
and the full topological neighborhood of all atoms. Chem. Central J. 3, 38-48.
Vedani, A., Dobler M., Lill, M.A., 2005. Combining protein modeling and 6D-
QSAR. Simulating the binding of structurally diverse ligands to the estrogen
receptor. J. Med. Chem. 48, 3700-3703.
Vedani, A., Dobler, M., 2002. 5D-QSAR: the key for simulating induced fit? J. Med.
Chem. 45, 2139-2149.
Veljkovic, V., Mouscadet, J.-F., Veljkovic, N., Glisic, S., Debyser, Z., 2007. Simple
criterion forselection of flavonoid compounds with anti-HIV activity. Bioorg.
Med. Chem. Lett. 17, 1226-1232.
Venkatesh, S., Lipper, R.A., 2000. Role of developmental scientist in compound lead
selection and optimization. J. Pharm. Sci. 89, 145-154.
Verloop, A., 1972. The use of linear free energy parameters and other experimental
constants in structure–activity studies, In: Aricens, E.J., (Ed.), Drug Design.
Volume 3, Academic Press, New York, pp. 133-187.
Verma, J., Khedkar, V.M., Coutinho, E.C., 2010. 3D-QSARin drug design-a review.
Curr. Top. Med. Chem. 10, 95-115.
Verma, R.P., Hansch, C., 2011. Use of 13
C-NMR chemical shift as QSAR/QSPR
descriptor. Chem. Rev. 111, 2865-2899.
References
326
Verpoorte, R., 1998. Exploration of nature's chemodiversity: the role of secondary
metabolites as leads in drug development. Drug Discovery Today. 3, 232-238.
Vogel, H.G., 1991. Similarities between various systems of traditional medicine.
Considerations for the future of ethno-pharmacology. J. Ethanopharmacol. 35,
179-190.
Vukicevic, D., 2011. Chor coefficient-measuring correlation in chemistry. MATCH
Commun. Math. Comput. Chem. 65, 365-382.
Vukicevic, D., Furtula, B., 2009. Topological index based on the ratios of geometrical
and arithmetical means of end-vertex degrees of edges. J. Math. Chem. 46,
1369-1376.
Wagener, M., Geerestein, V.J., 2000. Potential drugs and nondrugs: prediction and
identification of important structural features. J. Chem. Inf. Comp. Sci. 40, 280-292.
Walters, W.P., Stahl, M.T., Murcko, M.A., 1998. Virtural screening – An overview.
Drug Discov. Today. 3, 160-178.
Wan, Y.W., Sabbagh, E., Raese, R., Qian, Y., Luo, D., Denvir, J., Vallyathan, V.,
Castranova, V., Guo, N.L., 2010. Hybrid models identified a 12-gene signature
for lung cancer prognosis and chemoresponse prediction. PLoS One. 17, 5
e12222.
Wang, M., Tan, W., Zhou, J., Leow, J., Go, M., Lee, H.S., Casey, P.J. 2008. A small
molecule inhibitor of isoprenylcysteine carboxymethyltransferase induces
autophagic cell death in PC3 prostate cancer cells, J. Biol. Chem. 283, 18678-
18684.
Warne, P., Page, C., 2003. Is there a best strategy for drug discovery? Drug News
Perspect. 16, 177-182.
Weissman, G., 1991. Aspirin. Scientific American. 264, 84-90.
Welling, P.G., Lasagna, L., Banakar, U.V., 1996. The drug development process:
Increasing efficiency and cost effectiveness. New York: Marcel Decker, pp. 7-8.
Wiberg, K.B., 1968. Application of the Pople-Santry-Segal CNDO method to the
cyclopropylcarbinyl and cyclobutyl cation and to bicyclobutane. Tetrahedron.
24, 1083-1096.
Wiener, H., 1947a. Structural Determination of Paraffin Boiling Points. J. Am. Chem.
Soc. 69, 17-20.
References
327
Wiener, H., 1947b. Correlation of heat of isomerization and difference in heat of
vaporization of isomers among paraffin hydrocarbons. J. Am. Chem. Soc. 69,
2636-2638.
Wilkins, C.L., Randic, M., 1980. A graph theoretical approach to structure-property and
structure-activity correlation. Theoret. Chim. Acta. 58, 45-68.
Willett, P., Winterman, V., Bawden, D., 1986. Implementation of nonhierarchic
cluster analysis methods in chemical information structure search. J. Chem. Inf.
Comput. Sci. 26, 109-118.
Williams, M., 2002. Overview: Drug design and development: A perspective. In:
William, D.A., Lemke, T.L., (Eds.), Foye’s principal of medicinal chemistry. 5th
ed., Philadelphia: Lipponcott William & Wilkins, pp. 12-13.
Winkler, D.A., 2002. The role of quantitative structure-activity relationships in
bimolecular discovery. Briefings bioinform. 3, 73-86.
Winter-Vann, A.M., Casey, P.J., 2005. Post- prenylation- processing enzymes as new
targets in oncogenesis, Nat. Rev. Cancer. 5, 405–412.
Wold, S., 1976. Pattern recognition by means of disjoint principal components
models. Pattern Recogn. 8, 127-139.
Wold, S., Sjostrom, M., Eriksson, L., 2001. PLS-regression: a basic tool of
chemometrics. Chemom. Intell. Lab. Syst. 58, 109-130.
World Health Organization. 2009. Guidelines for surveillance of drug resistance in
tuberculosis. 4th ed., WHO/HTM/TB/2009.422. Geneva, Switzerland.
World Health Organization. 2013. International Agency for Research on Cancer
project GLOBOCAN 2012: estimated cancer incidence,mortality, prevalence
wordwide 2012 [online] Available at: http://www.who.int/mediacentre/fact
sheets/fs 297/en/ (Accessed on 25/07/2014).
Worth, A.P., Bassan, A., De Bruijn, J., Saliner, A.G., Netzeva, T., Patlewicz, G.,
Pavan, M., Tsakovska, I., Eisenreich, S., 2007. The role of the European
Chemicals Bureau in promoting the regulatory use of QSARs methods. SAR
QSAR Environ. Res. 18, 111-125.
Wu, X., Zeng, H., Zhu, X., Ma, Q., Hou, Y., Wu, X., 2013. Novel pyrrolopyridinone
derivatives as anticancer inhibitors towards Cdc7: QSAR studies based on
dockings by solvation score approach. Eur. J. Pharm. Sci. 20, 50(3-4), 323-34.
References
328
Xiangxiang, W., Xin, Z., Yimin, H., Xuefen, W., Huahui, Z., 2013. 3D-QSAR
Modeling Studies on Pyrrolopyridinone Derivatives as Inhibitors of Cdc7
Kinase. Lett. Drug Design Disc. 10(10) 995- 1006.
Xu, H., Agrafiotis, D.K., 2002. Retrospect and prospect of virtual screening in drug
discovery. Curr. Top. Med. Chem. 2, 1305-1320.
Xu, L., Zhang, Q.Y., Wang, J., Dong, L., 2006. Extended topological indices and
prediction of activities of chiral compounds. Chemom. Intell. Lab. Syst. 82, 37-
43.
Xu, X., Guo, Y., 2012. The edge version of eccentric connectivity index. Internat.
Math. Forum. 7, 273 -280.
Yang, Y.Q., Xu, L., Hu, C.Y., 1994. Extended adjacency matrix indices and their
applications. J. Chem. Inf. Comput. Sci. 34, 1140-1145.
Yao, Y., Xu, L., Yang, Y.Q., Yuan, X.S., 1993. Study on structure activity
relationships of organic compounds: three new topological indices and their
applications. J. Chem. Inf. Comput. Sci. 33, 590-594.
Young S.G., Ambroziak P., Kim E., Clarke S., 2000. Post-isoprenylation protein
processing: CXXX (CaaX) endoproteases and isoprenylcysteine carboxyl
methyltransferase. In: Tamanoi, F., Sigman, D.S. (Eds.), The Enzymes: Protein
lipidation. Academic Press, San Diego. pp. 155–213.
Yuan, H., Cao, C., 2003. Topological indices based on vertex, edge, ring, and
distance: application to various physicochemical properties of diverse
hydrocarbons. J. Chem. Inf. Comput. Sci. 43, 501-512.
Zhang, F.L., Casey, P.J., 1996. Protein prenylation: molecular mechanisms and
functional consequences, Annu. Rev. Biochem., 65, 241-269.
Zhang, Q., Hughes-Oliver, J.M., Ng, R.T., 2009. A model-based ensembling approach
for developing QSARs. J. Chem. Inf. Model. 49, 1857-1865.
Zhang, Q-U., Aires-de-Sousa, J., 2007. Random forest prediction of mutagenicity from
empirical physicochemical descriptors. J. Chem. Inf. Model. 47, 1-8.
Zhang, Y., 2005. The magic bullets and tuberculosis drug targets. Annu. Rev.
Pharmacol. Toxicol. 45, 529–564.
Zhang, Y., Gutman, I., Liu, J., Mu, Z., 2012. q-Analog of Wiener index. MATCH
Commun. Math. Comput. Chem. 67, 347-356.
References
329
Zhao, M., Li, Z., Wu, Y., Tang, Y.R., Wang, C., Zhang, Z., Peng, S., 2007. Studies on
log P, retention time and QSAR of 2-substituted phenylnitronyl nitroxides as
free radical scavengers. Eur. J. Med. Chem., 42, 955–965.
Zhou, B., Du, Z., 2010. On eccentric connectivity index. MATCH Commun. Math.
Comput. Chem. 63, 181-198.
Zhou, B., Trinajstic, N., 2008. A note on Kirchhoff index. Chem. Phy. Lett. 455, 120-
123.
Zhou, P., Zhou, Y., Wu, S., Li, B., Tian, F., Li, Z., 2006. A new descriptor of amino
acids based on the three-dimensional vector of atomic interaction fields.
Chinese Sci. Bull. 51, 524-529.
Zhu, F., Shi, Z., Qin, C., Tao, L., Liu, X., Xu, F., Zhang, L., Song, Y., Liu, X., Zhang,
J., Han, B., Zhang, P., Chen, Y., 2011. Therapeutic target database update 2012:
a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 1-9.
Zupan, J.A., Gasteiger, J., 1993. Neural Networks for Chemists, Wiley VCH,
Weinheim, Germany.