Next Article in Journal
Is Electronic Publishing Being Used in the Best Interests of Science? The Scientist’s View
Previous Article in Journal
Glycerol-Induced Aggregation of the Oligomeric L-Asparaginase II from E. coli Monitored with ATR-FTIR
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Maximum Topological Distances Based Indices as Molecular Descriptors for QSPR. 4. Modeling the Enthalpy of Formation of Hydrocarbons from Elements

CEQUINOR, Departamento de Química, Facultad de Ciencias Exactas, Universidad Nacional de la Plata, C.C. 962, La Plata 1900, Argentina
Andrey A. Toropov, Vostok Innovation Company, S. Azimstreet 4, 100047 Tashkent, Uzbekistan
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2001, 2(2), 121-132;
Received: 9 April 2001 / Accepted: 15 June 2001 / Published: 28 June 2001


The enthalpy of formation of a set of 60 hydroarbons is calculated on the basis of topological descriptors defined from the distance and detour matrices within the realm of the QSAR/QSPR theory. Linear and non-linear polynomials fittings are made and results show the need to resort to higher-order regression equations in order to get better concordances between theoretical results and experimental available data. Besides, topological indices computed from maximum order distances seems to yield  rather satisfactory predictions of heats of formation for hydrocarbons.

1. Introduction

Graphs have found considerable employment in several chemistry fields, particularly in modeling molecular structure [1,2,3,4,5,6,7,8,9,10]. The applications of graphs to the study of structure-property relationships implies the representation of molecules by selected molecular descriptors, often referred to as topological indices [11,12]. These topological indices, which often have a direct structural interpretation, are defined in terms of selected structural parts and hopefully should help one in building molecular models for structure-property relationships. Among hundreds of possible descriptors a few have arisen again and again as the most useful for characterization of molecules [13,14,15,16]
The graph theoretical characterization of molecular structure is realized by means of various matrices, polynomials, spectra, spectral moments, sequences counting distances, paths and walks. The molecular matrices represent an important source of structural descriptors. Usually, a small number of matrices is used to characterize the molecular topology, namely the adjacency, the distance and sometimes, the Laplacian matrix. Novel matrices were developed in recent years, encoding in various ways the topological information [17]. However, distance matrices remain being the most relevant ones within the realm of the Quantitative Structure Activity (Property) Relationships (QSAR/QSPR) theory.
Any matrix, the elements of which satisfy the axioms of distance, can be referred to as a distance matrix D ≡ {Dij} [18]. The axioms of distance require that
Distance is a positive quantity, Dij ≥ 0, assigned to a pair of elements (points in an n-dimensional vector space).
Dii = 0 ∀i = 1, 2, ......N; N = number of elements
Distance does not depend on the direction of measurement, i.e. Dij = Dji
Distance satisfies the triangular inequality, i.e. Dij ≤ Dik + Dkj
Two distance matrices are particularly important, both of them based on the topological distance for vertices within a graph: the distance matrix and the detour matrix. The detour matrix, together with the distance matrix, was introduced into the mathematical literature in 1969 by Frank Harary [19]. Both matrices were also briefly discussed in 1990 by Buckley and Harary [20]. The detour matrix was introduced into the chemical literature in 1994 under the name "the maximum path matrix of a molecular graph" [21,22]. The graph-theoretical detour matrix have found some interest in chemistry [23] and a valuable variation pertaining to the definition of the diagonal elements was proposed by Rücker and Rücker [24]. They found this matrix in combination with the Wiener index W is more useful than Hosoya's Z index for regression of the boiling points of a large sample of compounds containing all acyclic and cyclic alkanes with known boiling points from methane up to polycyclic octanes.
In three previous papers [25,26,27] we have analyzed the relative merits of these distances when they are used to define molecular descriptors in order to calculate physical chemistry properties within the realm of QSAR/QSPR theory. The aim of this paper is to continue with this sort of discussion resorting to the calculation of heats of formation for a representative set of 60 hydrocarbons.
The paper is organized as follows: next section deals with some basic mathematical definitions and the computational procedure; then we present the results and discuss them; and finally we state the conclusion of the present study as well as some possible extensions.

2. Basic Definitions

The adjacency matrix: The adjacency matrix A = A(G) of a graph G with N vertices is the square N x N symmetric matrix whose entry in the i th row and j th column is defined as [19]
Ijms 02 00121 i001
where E(G) represents the set of edges of G. The sum of entries over row i or column j in A(G) is the degree of vertex i, degi.
An example of molecular graph and the adjacency matrix is given below for the 1-ethyl-2-methylcyclopropane. The vertices and edges are labeled from 1 to 6 and from a to f, respectively.
Ijms 02 00121 i002
Ijms 02 00121 i003
The distance matrices
The distance matrix D(G) can be defined for G with elements Dij, the distance, or the number of least steps from vertex i to vertex j. Similarly, the detour matrix Δ(G) of a labeled connected graph G is a real symmetric N x N matrix whose (i,j) entry is the length of the longest path from vertex i to vertex j (i.e., the maximum number of edges in G between vertices i and j [23]).
For example, for the previous graph corresponding to 1-ethyl-2-methyl cyclopropane molecule, matrices A and Δ are:
Ijms 02 00121 i004
Topological molecular descriptors
We present the basic definitions related to the topological descriptors chosen for the present study.
W i e n e r   i n d e x   [ 28 ]  W  =   0.5   i , j  Dij
H a r a r y   i n d e x   [ 29 ]  H  =   0.5   i , j   D ij - 1
M T I   i n d e x   [ 30 , 31 ]  MTI  =   i   e i
where ei are elements of the row matrix v[A + D] = [e1, e2, e3, ...., eN]. v is the so-called valence matrix.
B a l a b a n   i n d e x   [ 32 , 33 ]  J  =   [ q / ( μ +   1 ) ]   i , j   ( d i   d j ) - 1 / 2
where d i   =   j D ij , q is the number of edges and μ the number of cycles in the graph. The summations in formulas (2), (3) and (5) are over all edges i-j in the hydrogen-depleted graph.
Z e r o   o r d e r   c o n n e c t i v i t y   i n d e x   [ 34 , 35 ]    0 χ =   i   ( δ i ) - 1 / 2
where δ i   =   j A ij is the degree of the i-th vertex.
R a n d i c s   c o n n e c t i v i t y   i n d e x   [ 32 ]    1 χ =   ij   ( δ i δ j ) - 1 / 2
G e n e r a l i z e d   c o n n e c t i v i t y   i n d e x   [ 35 ]    h χ =   paths   ( δ vo   δ vl   .... δ vh ) - 1 / 2
where the summation is taken over all possible paths of lengths 0, 1, ...., h.
T h e   Z a g r e b   g r o u p   i n d i c e s   [ 36 , 37 ]  M1  =   i   ( δ i ) 2
M2  =   i , j   δ i δ j
The employment of these topological descriptors has proven to be extremely useful in QSAR/QSPR studies giving simple correlations between the selected properties and the molecular structure [39,40]. Multiple regression analysis is often employed in such studies in the hope that it might point to structural factors that influence a particular property. Naturally, regression analysis results do no establish any sort of causal relationships between structural components and molecular properties. However, it may be helpful in model building and be useful in the design of molecules with prescribed desirable properties [41].
An interesting alternative to the previous definitions based upon distance matrix D is replace it by the detour matrix Δ, defining the associated topological indices W*, H*, etc. on the basis of this last matrix in a similar fashion as done in Eqs. (2-5).

3. Results and Discussion

We have employed two sets of topological descriptors; a) {Nc, 0χ, 1χ, 2χ, M1, M2, W, H, H, J, MTI} and b) {Nc, 0χ, 1χ, 2χ, M1, M2, W, H, J, MTI, W*, H*, J*, MTI*}. Nc stands for the number of C atoms. This particular choice was made since we want to know the relative merits of topological indices defined on the basis of the two distance matrices (i.e. D and Δ). A way to assess it is via this choice, although there are other options.
The molecular set comprises 60 hydrocarbons containing from 1 to 18 carbon atoms and they are presented in Table 1 together with their corresponding topological parameters.
Table 1. Topological parameters for hydrocarbons.
Table 1. Topological parameters for hydrocarbons.
1. ethane22.0002.0000.0002211.0001.000411.0001.0004
2. propane32.7072.8281.4146842.5001.6331642.5001.63316
3. butane43.4143.8282.0001016104.3331.97538104.3331.97538
4. 2-methylpropane43.5773.4643.464121894.5002.3243694.5002.32436
5. pentane54.1214.8282.7071424206.4172.19174206.4172.19174
6. 2-methylbutane54.2844.5403.6041628186.6672.54068186.6672.54068
7. 2,2-dimethyl-propane54.5004.0006.0002032167.0003.02464167.0003.02464
8. hexane64.8285.8283.4141832358.7002.339128358.7002.339128
9. 2-methylpentane64.9925.5404.3652036329.0002.627118329.0002.627118
10. 3-methylpentane64.9925.6163.8432038319.0832.754114319.0832.754114
11. 2,2-dimethyl-butane65.2075.1215.8282444289.5003.168106289.5003.168106
12. 2,3-dimethyl-butane65.1555.2854.9762242299.3332.993108299.3332.993108
13. heptane75.5366.8284.12122405611.1502.4472045611.1502.447204
14. 3-methylhexane75.6996.6164.60424465011.6172.8321825011.6172.832182
15. 2,2-dimethyl-pentane75.9146.1216.62128524612.0833.1541704612.0833.154170
16. 2,2,3-trime-thylbutane76.0775.8877.04130604212.5003.5411564212.5003.541156
17. 3-methylheptane86.4067.6165.31128547614.2672.8622767614.2672.862276
18. 4-methylheptane86.4067.6165.36528547514.3172.9202727514.3172.920272
19. 2,2-dimethylhexane86.6217.1217.32823607114.7673.1122607114.7673.112260
20. 2,3-dimethylhexane86.5697.3616.02030607014.7333.1712547014.7333.171254
21. 2,4-dimethylhexane86.5697.3286.28630587114.6503.0992587114.6503.099258
22. 2,5-dimethylhexane86.5697.2526.73030567414.4672.9282707414.4672.928270
23. 3,3-dimethylhexane86.6217.2436.53632646715.0333.3732446715.0333.373244
24. 1,3-dime-thylbenzene85.9837.5756.75436766116.0832.2312681237.8331.071516
25. 1,2-dime-thylbenzene85.9837.6096.47836786016.1672.2792641247.8101.061520
26. 1,4-dime-thylbenzene85.9837.575 6.73036766216.0332.1922721227.8671.082512
28.1-methyl-4-ethylbenzene 96.6908.6517.06840869019.0672.18038116210.521.171669
29. 1,2,3-trime-thylbenzene96.8538.4307.48942948219.6672.41334916410.1021.152677
30. 1,2,4-trime-thylbenzene96.8538.3977.74642928419.5332.34635716210.1601.168669
31. 1,3,5-triethyl-benzene96.8538.3638.04542908419.5002.34135716210.1501.167669
32. 1,3-triethyl-benzene107.3979.7277.430449612122.3832.24650021312.8821.224868
33. 1,4-diethyl-benzene107.3979.7277.406449612522.2432.17451620912.9811.254852
35. 1,2,3-triethyl-benzene128.97511.6588.6795412419030.2332.52476033818.1921.3431352
36. 1,2,4-triethyl-benzene128.97511.6258.8485412219829.8762.41379233018.3651.3791320
37. 1,3,5-triethyl-benzene128.97511.5919.0605412019829.8002.40579233018.3391.3771320
41. 1-methylna-phthalene117.68210.7549.2335613014027.8501.9936464268.0240.6261898
42. 2-methylna-phthalene117.68210.7209.4465612814427.633 1.932 6644248.0490.6291890
43. 1-ethylna-phthalene128.38911.8309.6156014018231.5331.98781651810.4440.6622280
44. 2-ethylna-phthalene128.38911.7969.7486013819031.1761.89585251410.4890.6672264
45. 1,2-dimethyl-naphthalene128.55211.57510.2446214617831.9172.02780051610.1660.6642270
46. 1,3-dimethyl-naphthalene128.55211.54210.5256214417931.8332.01580451510.1750.6652266
47. 1,4-dimethyl-naphthalene128.55211.57510.2886214617632.0002.05579051610.1610.6642270
48. 1,5-dimethyl-naphthalene128.55211.57510.3086214617532.0502.06678651710.1500.6632274
49. 1,6-dimethyl-naphthalene128.55211.54210.5016214418031.7832.00380851410.1860.6672262
50. 1,7-dimethyl-naphthalene128.55211.54210.5016214418131.7501.99281251510.1750.6652266
51. 2,3-dimethyl-naphthalene128.55211.54210.46162144182 31.7001.97681851410.1910.6672262
52. 2,6-dimethyl-naphthalene128.55211.50810.7136214218531.5331.94483051110.2250.6712250
53. 2,7-dimethyl-naphthalene128.55211.50810.7136214218631.5101.93683451210.2110.6692254
54. 1-propylna-phthalene139.09612.83010.3766414823635.1101.940103662213.1110.6952712
55. 2-propylna-phthalene139.09612.79010.5456414624834.6541.837109061613.1730.7022688
56. 2-ethyl-3-methylnaphthalene139.25912.61810.8436615423235.4931.965102061612.7140.7012682
57. 2-ethyl-6-me-thylnaphthalene139.25912.58411.0516615223835.2191.915104461012.7760.7082658
58. 2-ethyl-7-me-thylnaphthalene139.25912.58411.0516615224035.1771.902105261212.7510.7062666
59. 1-butylna-phthalene149.80313.83011.0836815630338.6461.876131073915.9570.7263198
60. 2-butylna-phthalene149.80313.79611.2526815431938.1171.772138273116.0330.7343166
Although at first sight this rather specialized set includes molecules with only C and H atoms, this option does not necessarily implies a lack of molecular variation within such particular choice. As a matter of fact, the hydrocarbon set includes examples of planar, non-planar, alternant and non-alternant aromatic hydrocarbons, alkyl- and alkenyl-substituted benzene derivatives, acyclic and polycyclic alkanes, strained and unstrained olefins and disparate structures combined with aromatics like benzene and naphthalene, which do not require separate parametrizations for different types of C and H atoms. This molecular set has been used in previous studies on QSPR theory [42,43,44].
Enthalpy (or heat) of formation is a fundamental thermodynamical key for predicting the compound's thermochemical behavior. Thus, enthalpies of formation are important in the investigation of bond energies, resonance energies, the nature of the chemical bond, the calculation of equilibrium constants of reaction, etc. [45]. Therefore, it is not surprising that considerable endeavor has been directed towards the determination of heats of formation in the past [46,47,48,49,50,51]. Although a wide variety of procedures to calculate heats of formation theoretically have indeed been introduced, based on such different concepts as isodesmic and homodesmic reactions, atom and group equivalents, transferability and additivity of Fock matrix elements, etc. [52,53,54,55,56,57,58,59,60] the calculation through QSPR theory has not attracted so much attention.
We have performed a complete analysis to find the best one-, two-, ..., five-variables fittings at first, second and third polynomial orders. Statistical results are given in Table 2 for both molecular sets, while in Table 3 we display some theoretical results together with the experimental data. We have inserted the best five-variables third-order correlations for both topological indices sets a) and b). Complete results are available and can be obtained upon request to one of us (E.A.C.) at the above address.
Table 2. Statistical results for the best fitting equations.
Table 2. Statistical results for the best fitting equations.
DescriptorsFirst orderSecond orderThird order
Molecular set 1RsFRsFRsF
0χ, M10.974986.19445480.979045.87893170.980795.7355223
0χ, 1χ, M10.989384.05068650.998071.826922790.998171.83161511
Nc, 1χ, M1, H0.992103.49688590.996752.41479750.997282.3017716
Nc, 0χ, M1, H, MTI0.996372.372914790.998491.680216180.999411.11132470
Molecular set 2
1χ, H*0.979105.66786600.986204.77864870.987334.6685341
M1, H*, J*0.994093.0262415640.996522.450012630.997102.3052952
W, J, MTI, J*0.997192.086824380.997791.992014360.998911.45881790
Nc, 0χ, M1, J, J*0.999061.210357150.999371.081139140.999590.92923534
Table 3. Experimental and theoretical heats of formation (kcal/mol) for a set of 60 hydrocarbons. The numbering of molecules corresponds to the molecular listing of Table 1.
Table 3. Experimental and theoretical heats of formation (kcal/mol) for a set of 60 hydrocarbons. The numbering of molecules corresponds to the molecular listing of Table 1.
MoleculeΔHof (exp) [45/ΔHof (theor)(1)ΔHof (theor)(2)
55.17.65 17.7817.22
Average absolute deviation-0.760.62
(1) Best five-variables fitting for molecular set a).(2) Best five-variables fitting for molecular set b).
Analysis of results from Table 2 and Table 3 shows the better predictive power arising from the fitting equations derived on the basis of set b) of topological descriptors than those corresponding to the set a). The statistical parameters (Table 2) and the correlations between experimental data and theoretical predictions (Table 3) makes clear the convenience of resorting to the use of the detour matrix instead of the standard distance matrix in order to define the pertinent topological parameters. Besides, it seems recommendable to employ higher-order polynomials to get a better degree of prediction. These conclusions are in line with other previous ones in some studies on the usefulness of the application of the detour matrix [25,26,27].
In order to properly judge the value of the predictions is interesting to note the low average absolute deviations (0.76 and 0.62 kcal/mol, respectively). It must be taken into account that usually the theoretical predictions give an average absolute deviation around 2 kcal/mol, which is the degree of uncertainty in the experimental determinations. Furthermore, there is not any "pathological" prediction and the maximum deviation for the results presented in Table 3 is 2.31 kcal/mol.

4. Conclusions

We have employed some usual topological indices to study the heat of formation of a set of hydrocarbons comprising 60 structurally diverse molecules. In those cases were the definition demands the employment of the distance matrix we have also defined similar indices on the base of the detour matrix (i.e. maximum distance) in order to assess the relative merits of both distance definitions. Results show that resorting to the detour matrix for defining the topological indices yields better correlations to predict enthalpies of formation. These results agree with other similar ones to study other physical chemistry properties, which seems to support the use of detour indices in structure-property modeling [25,26,27,61,62,63]. We conclude that the obtained results are good enough for the chosen set to validly infer that the detour matrix Δ represents a convenient topological device to be employed in the QSAR/QSPR analysis and it constitutes a valuable molecular descriptor which verily adds to the set of topological matrices. In order to arrive to more significant and definite conclusions on this issue, we deem it is necessary to extend the calculations to quite different molecular sets and other physical chemistry properties and biological activities. Research along this line is being carried in our laboratories and results will be presented elsewhere in the near future.


  1. Balaban, A. T. (Ed.) Chemical Applications of Graph Theory; Academic Press: London, 1976.
  2. King, R. B. (Ed.) In Chemical Applications of Topology and Graph Theory; Studies in Physical and Theoretical Chemistry 28. Elsevier: Amsterdam, 1983.
  3. Balasubramanian, K. Chem. Rev. 1985, 85, 599.
  4. Trinajstic, N. Mathematics and Computational Concepts in Chemistry; Ellis Horwood: Chichester, 1986. [Google Scholar]
  5. King, R. B.; Rouvray, D. H. (Eds.) In Graph Theory and Topology in Chemistry; Studies in Physical and Theoretical Chemistry. Elsevier: Amsterdam, 1987.
  6. Lacher, L. C. (Ed.) In MATH/CHEM/COMP 1987; Studies in Physical and Theoretical Chemistry 54. Elsevier: Amsterdam, 1988.
  7. Rouvray, D. H. (Ed.) Computational Graph Theory; Nova Sci. Publ.: New York, 1990.
  8. Klein, D. J.; Randic, M. (Eds.) Mathematical Chemistry; VCH: Weinheim, 1990.
  9. Graovac, A. (Ed.) In MATH/CHEM/COMP 1988; Studies in Physical and Theoretical Chemistry 63. Elsevier: Amsterdam, 1989.
  10. Trinajstic, N. Chemical Graph Theory; CRC Press: Boca Raton, FL, 1992. [Google Scholar]
  11. Balaban, A. T.; Motoc, I.; Bonchev, D.; Mekenyan, O. Top. Curr. Chem. 1983, 114, 21.
  12. Randic, M. J. Math. Chem. 1992, 9, 97.
  13. Katritzky, A. R.; Gordeeva, E. V. J. Chem. Inf. Comput. Sci. 1993, 33, 835. [CrossRef]
  14. Basak, S. C.; Magnuson, V. R.; Niemi, J. G.; Regal, R. R.; Vetih, G. D. Math. Modell. 1986, 8, 300. [CrossRef]
  15. Randic, M. Croat. Chem. Acta 1993, 66, 289.
  16. Randic, M.; Trinajstic, N. J. Mol. Struct. THEOCHEM 1993, 284, 209. [CrossRef]
  17. Ivanciuc, O.; Ivanciuc, T.; Diudea, M. V. SAR/QSAR Environm. Res. 1997, 7, 63.
  18. Randic, M.; Kleiner, A. F.; DeAlba, L. M. J. Chem. Inf. Comput. Sci. 1994, 34, 277. [CrossRef]
  19. Harary, F. Graph Theory; Addison-Wesley: Reading, MA, 1969; p. 203. [Google Scholar]
  20. Buckely, F.; Harary, F. Distance in Graphs; Addison-Wesley: Reading, MA, 1990; pp. 213–214. [Google Scholar]
  21. Ivanciuc, O.; Balaban, A. T. Commun. Math. Chem. (MATCH) 1994, 30, 141.
  22. Amic, D.; Trinajstic, N. Croat. Chem. Acta 1995, 68, 53.
  23. Trinajstic, N.; Nikolic, S.; Lucic, B.; Amic, D.; Mihalic, Z. J. Chem. Inf. Comput. Sci. 1997, 37, 631.
  24. Rücker, G.; Rücker, C. J. Chem. Inf. Comput. Sci. 1998, 38, 710.
  25. Firpo, M.; Gavernet, L.; Castro, E. A.; Toropov, A. A. J. Mol. Struct . THEOCHEM 2000, 501-502, 419. [CrossRef]
  26. Castro, E. A.; Tueros, M.; Toropov, A. A. Comp. & Chem. 2000, 24, 571.
  27. Tueros, M.; Castro, E. A.; Toropov, A. A. J. Mol. Model. (in press)
  28. Wiener, H. J. Am. Chem. Soc. 1947, 69, 17, 1947, 69, 2336; J. Chem. Phys. 1947, 15, 766; J. Phys. Chem. 1948, 52, 425; 1948, 52, 1082.
  29. Plavsic, D; Nikolic, S.; Trinajstic, N.; Mihalic, Z. J. Math. Chem. 1993, 12, 235. [CrossRef]
  30. Schultz, H. P. J. Chem. Inf. Comput. Sci. 1989, 29, 237.
  31. Mueller, W. R.; Szymanski, K.; Knop, J. V.; Trinajstic, N. J. Chem. Inf. Comput. Sci. 1990, 30, 169. [CrossRef]
  32. Randic, M. J. Am. Chem. Soc. 1975, 97, 6609. [CrossRef]
  33. Balaban, A. T. Chem. Phys. 1982, 89, 399.
  34. Balaban, A. T. J. Chem. Inf. Comput. Sci. 1985, 25, 334. [CrossRef]
  35. Kier, L. B.; Hall, H. Molecular Connectivity in Structure-Activity Analysis; Research Studies Press: Ukm, Letchworth, Hertfordshire, 1986. [Google Scholar]
  36. Gutman, I.; Trinajstic, N. Chem. Phys. Lett. 1972, 17, 535.
  37. Gutman, I.; Ruscic, B.; Trinajstic, N.; Wilcox, Jr. C. F. J. Chem. Phys. 1975, 62, 3399.
  38. Toropov, A. A.; Toropova, A. P.; Ismailov, T. T.; Bonchev, D. J. Mol. Struct. THEOCHEM 1998, 424, 237.
  39. Dearden, J. C. Chem. Intell.Lab. Syst. 1994, 24, 77.
  40. Basak, S. C.; Niemi, G. J.; Veith, G. D. J. Math. Chem. 1991, 7, 243. [CrossRef]
  41. Randic, M.; Trinajstic, N. New J. Chem. 1994, 18, 179.
  42. Castro, E. A. Comput. Chem. 1997, 21, 305.
  43. Mercader, A.; Castro, E. A.; Toropov, A. A. Chem. Phys. Lett. 2000, 330, 612.
  44. Herndon, W. C. Chem. Phys.Lett. 1995, 234, 82.
  45. Stull, D. R.; et al. The Chemical Thermodynamics of Organic Compounds; Wiley: New York, 1969. [Google Scholar]
  46. Pedley, J. D.; et al. Thermochemical Data of Organic Compounds, 2nd. ed.; Chapman and Hall, 1986. [Google Scholar]
  47. Cox, J. D.; Pilcher, G. Thermochemistry of Organic and Organometallic Compounds; Academic Press: London, 1970. [Google Scholar]
  48. Pedley, J. D.; et al. Computer Analysed Thermochemical Data: Organic and Organometallic Compounds; University of Sussex: Sussex, 1977. [Google Scholar]
  49. Wagman, D.D.; et al. Chem. Ref. Data Suppl. 1982, No 222, 11.
  50. Benson, S. W. Thermochemical Kinetics, 2nd. ed.; Wiley: New York, 1987. [Google Scholar]
  51. Lowry, T. H.; Richardson, K. S. Mechanism and Theory in Organic Chemistry, 3rd. ed.; Harper & Row: New York, 1987. [Google Scholar]
  52. Habibollahzadeh, D.; et al. J. Comput. Chem. 1995, 16, 654. [CrossRef]
  53. Ibrahim, M. B.; Schleyer, P. v. R. J. Comput. Chem. 1985, 6, 157. [CrossRef]
  54. Yala, Z. J. Molec. Struct. THEOCHEM 1990, 207, 217. [CrossRef]
  55. Castro, E. A. J. Molec. Struct. THEOCHEM 1994, 304, 93. [CrossRef]
  56. Castro, E. A. J. Molec. Struct. THEOCHEM 1995, 339, 239. [CrossRef]
  57. Dewar, J. M. S.; Storch, D. M. J. Am. Chem. Soc. 1985, 107, 3898. [CrossRef]
  58. Castro, E. A. J. Chem. Soc. Pak. 1995, 17, 156.
  59. Vericat, C.; Castro, E. A. Commun. Math. Comp. Chem. MATCH 1996, 34, 167.
  60. Vericat, C.; Castro, E. A. Egyp. J. Chem. 1998, 41, 109.
  61. Lukovits, I. Croat. Chem. Acta 1995, 68, 873.
  62. Lukovits, I. J. Chem. Inf. Comput. Sci. 1996, 36, 65.
  63. Zhu, H. –Y.; Klein, D. J.; Lukovits, I. J. Chem. Inf. Comput. Sci. 1996, 36, 420. [CrossRef]

Share and Cite

MDPI and ACS Style

Mercader, A.; Castro, E.A.; Toropov, A.A. Maximum Topological Distances Based Indices as Molecular Descriptors for QSPR. 4. Modeling the Enthalpy of Formation of Hydrocarbons from Elements. Int. J. Mol. Sci. 2001, 2, 121-132.

AMA Style

Mercader A, Castro EA, Toropov AA. Maximum Topological Distances Based Indices as Molecular Descriptors for QSPR. 4. Modeling the Enthalpy of Formation of Hydrocarbons from Elements. International Journal of Molecular Sciences. 2001; 2(2):121-132.

Chicago/Turabian Style

Mercader, Andrés, Eduardo A. Castro, and Andrey A. Toropov. 2001. "Maximum Topological Distances Based Indices as Molecular Descriptors for QSPR. 4. Modeling the Enthalpy of Formation of Hydrocarbons from Elements" International Journal of Molecular Sciences 2, no. 2: 121-132.

Article Metrics

Back to TopTop