Organometallic compounds are an important class of chemicals, because many of them have vital biochemical features. There are studies on the quantitative structure-property/activity relationships (QSPR/QSAR) for organic substances [1-5], but only a few articles have deal with QSPR for organometallic compounds  Simplified molecular input line entry system (SMILES) [9-13] has been used as an alternative for molecular graphs in the QSPR/QSAR analyses  SMILES-based optimal descriptors were calculated a

A A Toropov

A P Toropova

E Benfenati

CiteSeerX

Chemical Physics Letters 461 (2008) 343–347Contents lists available at ScienceDirect
Chemical Physics Letters
journal homepage: www.elsevier .com/locate /cplet tQSPR modeling for enthalpies of formation of organometallic compounds
by means of SMILES-based optimal descriptors
A.A. Toropov a,b,*, A.P. Toropova a,b, E. Benfenati b
a Institute of Geology and Geophysics, Khodzhibaev Street 49, 100041 Tashkent, Uzbekistan
b Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
a r t i c l e i n f o a b s t r a c tArticle history:
Received 20 May 2008
In final form 8 July 2008
Available online 15 July 20080009-2614/$ - see front matter  2008 Elsevier B.V. A
doi:10.1016/j.cplett.2008.07.027
* Corresponding author. Address: Institute of Geolo
baev Street 49, 100041 Tashkent, Uzbekistan.
E-mail address: aatoropov@yahoo.com (A.A. ToropA quantitative structure–property relationship (QSPR) model for a predicting gas-phase enthalpy of for-
mation have been developed, using as chemical information descriptors based on the simplified molec-
ular input line entry system (SMILES). The model is one-variable equation. The SMILES-based descriptors
calculated with correlation weights of SMILES attributes which are obtained by the Monte Carlo method.
The model addressed organometallic compounds. Statistical characteristics of the model are the follow-
ing: n = 104, R2 = 0.9943, Q2 = 0.9940, s = 19.9 (kJ/mol), F = 17701 (training set); n = 28, R2 = 0.9908,
Q2 = 0.9892, s = 29.4 (kJ/mol), F = 2788 (test set).
 2008 Elsevier B.V. All rights reserved.Organometallic compounds are an important class of chemicals,
because many of them have vital biochemical features. There
are studies on the quantitative structure–property/activity
relationships (QSPR/QSAR) for organic substances [1–5], but only
a few articles have deal with QSPR for organometallic compounds
[6–9]. A major problem with organometallic compounds is that
many of the software calculating chemical descriptors are not
suitable for metals.
Simplified molecular input line entry system (SMILES) [9–13]
has been used as an alternative for molecular graphs in the
QSPR/QSAR analyses [14–17]. The aim of the present study is the
estimation of the SMILES-based optimal (flexible) descriptors in
QSPR modeling of enthalpies of formation from elements for orga-
nometallic compounds. Numerical data and the split into the train-
ing (n = 104) and test (n = 28) sets were taken from [6]. Canonical
SMILES notations have been generated with ACD/ChemSketch soft-
ware [9].
SMILES-based optimal descriptors were calculated as
DCW ¼ PCWðskÞ ð1Þ
where sk are SMILES attributes (SAk). The SMILES attribute (invari-
ant) can be a symbol of the SMILES notation (for instance, ‘c’, ‘C’,
‘N’, ‘)’, ‘ = ’, etc.), or two symbols of the SMILES encoding a physico-
chemical image (for instance, ‘Cl’, ‘Si’, ‘Pb’, etc.); CW(x) is the
correlation weight for a SMILES attribute x. The CWs are calculated
by the Monte Carlo method optimization procedure [11–13] that
provides CWs values which used in Eq. (1) give a maximum for
the correlation coefficient between the descriptor and modelledll rights reserved.
gy and Geophysics, Khodzhi-
ov).parameter. Symbols ‘)’ and ‘]’ have been replaced by ‘(’ and ‘[’, be-
cause these are indicators of the same molecular phenomenon
(branching and ions, respectively [9–13]).
In fact, each SMILES attribute is a representation of molecular
structure: i.e., chemical elements, covalent bonds (double, triple),
cis- and trans isomerism, and others [10–13]. The additive scheme
[18] is a well-known approach of modeling properties by the selec-
tion of special additive contributions for molecular fragments. The
SMILES-based optimal descriptors that is calculated with Eq. (1) is
a similar approach, but multiplicative one. In principle, additive
optimal descriptors which are also calculated with SMILES can be
utilized in QSPR/QSAR analysis [19,20]. The algorithm of the Monte
Carlo optimization (used for both additive and multiplicative
schemes) is described in Ref. [21]. The algorithm has been checked
in many QSPR/QSAR studies [7,8,15–17].
The list of the SMILES attributes and their correlation weights
for three probes of the Monte Carlo method optimization are rep-
resented in Table 1. The one-variable model for the gas-phase en-
thalpy of formation of organometallic compounds (probe 1) is the
following:
Df H
0ðkJ=molÞ ¼ 22091:00 ð18:33Þ þ 22007:12 ð18:28ÞDCW
ð2Þ
n = 104, R2 = 0.9943, Q2 = 0.9940, s = 19.9 (kJ/mol), F = 17701
(training set);
n = 28, R2 = 0.9908, Q2 = 0.9892, s = 29.4 (kJ/mol), F = 2788 (test
set)
An example of the DCW calculation is given in Table 2. Calcula-
tions with Eq. (2) are demonstrated in Table 3. Structures of the
organometallic compounds are represented in Supplementary
material.
Table 1
Correlation weights for SMILES attributes obtained in three probes of the Monte Carlo
optimization
Sk CW (Sk) in probe 1 CW (Sk) in probe 2 CW (Sk) in probe 3
# 1.0090556 1.0074964 1.0101665
( 0.9998463 0.9998668 0.9998257
1 0.9996351 0.9999719 0.9998839
2 1.0009747 1.0008821 1.0010828
3 0.9996972 0.9998870 0.9996963
4 1.0001354 1.0004646 1.0004339
= 1.0042279 1.0039084 1.0050545
B 1.0017499 1.0026516 1.0025670
Al 1.0000149 1.0002363 0.9976983
C 0.9991618 0.9993168 0.9990561
As 1.0031238 1.0027897 1.0012775
Bi 1.0128181 1.0108004 1.0122084
Br 0.9940644 0.9951565 0.9932889
Cl 0.9925434 0.9938692 0.9915754
Ga 1.0012293 1.0012349 0.9990758
Ge 0.9990244 0.9993272 0.9964955
H 1.0022120 1.0018444 1.0024865
Hg 1.0059134 1.0050967 1.0044166
I 0.9961791 0.9968911 0.9956879
O 0.9963139 0.9964762 0.9954539
P 1.0029297 1.0038841 1.0041153
Pb 1.0090902 1.0077529 1.0080435
Sb 1.0055415 1.0048487 1.0040477
Se 1.0009714 1.0010277 0.9987955
Si 0.9947625 0.9958890 0.9917372
Sn 1.0038262 1.0033491 1.0019871
Te 1.0028037 1.0025096 1.0008769
[ 1.0020271 1.0021860 1.0037950
C 1.0008545 1.0006440 1.0009193
Table 2
Example of the DCW calculation: SMILES = ‘CC(C)C[Al](CC(C)C)CC(C)C’; no. 1;
DCW = 0.9927988
Sk CW (Sk) in probe 1
C 0.9991618
C 0.9991618
( 0.9998463
C 0.9991618
( 0.9998463
C 0.9991618
[ 1.0020271
Al 1.0000149
[ 1.0020271
( 0.9998463
C 0.9991618
C 0.9991618
( 0.9998463
C 0.9991618
( 0.9998463
C 0.9991618
( 0.9998463
C 0.9991618
C 0.9991618
( 0.9998463
C 0.9991618
( 0.9998463
C 0.9991618
Table 3
Experimental and calculated with Eq. (2) gas-phase DfH0 (kJ/mol) for the aliphatic organo
No.a SMILES
Training set
1 CC(C)C[Al](CC(C)C)CC(C)C
2 CCCC[Al](CCCC)CCCC
4 C[Al](C)C
5 CCC[Al](CCC)CCC
6 C[As](C)C
8 CC(C)CCB(CCC(C)C)CCC(C)C
9 CC(C)CB(CC(C)C)CC(C)C
10 CC(C)B(C(C)C)C(C)C
13 CCCCB(Br)CCCC
14 CCCCB(Cl)CCCC
15 CCCCB(I)CCCC
16 CCCCB(CCCC)CCCC
18 CCB(CC)CC
19 CCCCCCB(CCCCCC)CCCCCC
20 CCCCCCCB(CCCCCCC)CCCCCCC
21 CB(C)C
22 CCCCCCCCB(CCCCCCCC)CCCCCCCC
23 ClB(Cl)c1ccccc1
25 CCCC[Bi](CCCC)CCCC
26 C[Bi](C)C
28 CCC[Bi](CCC)CCC
30 CCCC[Ga](CCCC)CCCC
31 CC[Ga](CC)CC
32 C[Ga](C)C
33 CCC[Ga](CCC)CCC
35 O@C(c1ccccc1)[Ge](C(@O)c2ccccc2)(C(@O)c3ccccc3)C(@O)c4ccccc4
36 CC[Ge](CC)CC
37 CC[Ge](CC)(CC)CC
38 Br[Ge](C)(C)C
41 c1ccc(cc1)[Ge]2(CCCC2)c3ccccc3
42 C(#Cc1ccccc1)[Ge](c2ccccc2)(c3ccccc3)c4ccccc4
43 C@C[Ge](c1ccccc1)(c2ccccc2)c3ccccc3
44 c1ccccc1[Ge](c2ccccc2)(c3ccccc3)c4ccccc4
45 CCC[Ge](CCC)(CCC)CCC
46 [Hg](C#Cc1ccccc1)C#Cc2ccccc2
47 CC(C)CC[Hg]CCC(C)C
344 A.A. Toropov et al. / Chemical Physics Letters 461 (2008) 343–347The statistical characteristics of QSPR for the enthalpy of forma-
tion, with the same split into training and test sets, which have
been reported in [6] are R2 = 0.988, s = 29.1 (training set) andmetallic compounds
DCW Exp. Calcd.
0.9927988 231.500 242.357
0.9937151 216.000 222.192
1.0012427 86.500 56.531
0.9962180 153.300 167.111
1.0043555 12.500 11.972
0.9880130 381.800 347.680
0.9905015 280.300 292.915
0.9929963 251.400 238.011
0.9888422 300.400 329.432
0.9873291 365.700 362.729
0.9909458 225.500 283.137
0.9914157 287.000 272.796
0.9964162 152.700 162.749
0.9864403 395.000 382.291
0.9839619 454.800 436.832
0.9989259 122.600 107.518
0.9814898 514.600 491.236
0.9909081 266.100 283.967
1.0064377 71.200 57.795
1.0140617 194.400 225.577
1.0089726 133.900 113.582
0.9949219 226.900 195.635
0.9999401 62.300 85.199
1.0024586 35.900 29.772
0.9974278 125.500 140.487
1.0215911 388.200 391.278
0.9977380 111.900 133.660
0.9957599 164.900 177.192
0.9940083 222.200 215.740
1.0100168 138.900 136.560
1.0313527 578.900 606.104
1.0212093 362.600 382.877
1.0241275 438.600 447.097
0.9924257 229.700 250.570
1.0364297 720.500 717.833
1.0009463 82.700 63.054
Table 3 (continued)
No.a SMILES DCW Exp. Calcd.
48 CC(C)C[Hg]CC(C)C 1.0026264 38.300 26.081
49 CC(C)[Hg]C(C)C 1.0043092 37.000 10.954
50 CC(C)[Hg]Br 1.0011705 53.800 58.121
51 Br[Hg]CC 1.0023185 30.300 32.856
53 Br[Hg]CCC 1.0014784 51.400 51.345
56 CC(C)[Hg]Cl 0.9996386 86.900 91.834
57 CC[Hg]Cl 1.0007849 67.700 66.607
58 C[Hg]Cl 1.0016244 55.100 48.132
60 CC[Hg]CC 1.0066138 75.000 61.671
61 CC(C)[Hg]I 1.0033003 5.800 11.250
63 C[Hg]I 1.0052934 21.600 32.612
64 CCC[Hg]I 1.0036089 3.700 4.459
65 C[Hg]C 1.0083034 92.400 98.853
66 c1ccccc1[Hg]c2ccccc2 1.0216447 394.200 392.458
67 CCC[Hg]CCC 1.0049271 30.300 24.551
69 C[Pb](C)(C)C 1.0091720 136.100 117.970
70 Br[Pb](Br)(c1ccccc1)c2ccccc2 1.0121183 177.700 182.810
71 I[Pb](I)(c1ccccc1)c2ccccc2 1.0164292 288.200 277.679
72 Br[Pb](c1ccccc1)(c2ccccc2)c3ccccc3 1.0227730 406.300 417.289
73 I[Pb](c1ccccc1)(c2ccccc2)c3ccccc3 1.0249488 455.400 465.172
76 c1ccccc1P(c2ccccc2)c3ccccc3 1.0187786 328.400 329.384
78 CC[Sb](CC)CC 1.0042467 48.700 9.578
80 c1ccccc1[Sb](c2ccccc2)c3ccccc3 1.0255770 435.400 478.996
81 CCC[Sb](CCC)CCC 1.0017236 37.700 45.948
82 CC(C)[Se]C(C)C 0.9993752 108.000 97.631
84 CC[Se]CC 1.0016684 57.300 47.163
85 C[Se]C 1.0033497 17.800 10.164
86 CCCCC[Se]CCCCC 0.9966415 172.700 157.790
87 c1ccccc1[Se]c2ccccc2 1.0166255 289.700 281.999
88 CC[Si](CC)(CC)CC 0.9915120 265.700 270.677
90 Br[Si](C)(C)C 0.9897678 297.500 309.061
91 C[Si](C)(C)Cl 0.9882534 354.000 342.389
92 C[SiH](Cl)Cl 0.9850069 415.000 413.835
93 Cl[Si](Cl)(c1ccccc1)c2ccccc2 0.9946967 208.800 200.590
94 CC(C)C[Sn](CC(C)C)(CC(C)C)CC(C)C 0.9926349 272.200 245.964
95 CC(C)[Sn](C(C)C)(C(C)C)C(C)C 0.9959699 119.400 172.571
96 CCCC[Sn](Br)(CCCC)CCCC 0.9912768 270.600 275.852
97 CCCC[Sn](CCCC)(CCCC)CCCC 0.9938566 217.400 219.078
98 CC[Sn](Cl)(CC)CC 0.9947522 193.300 199.369
99 CC[Sn](CC)CC 1.0025336 54.700 28.122
100 CC[Sn](CC)(CC)CC 1.0005460 42.000 71.864
101 CC[Sn](Cl)(Cl)Cl 0.9832651 429.300 452.168
102 CC[Sn](c1ccccc1)(c2ccccc2)c3ccccc3 1.0217977 380.000 395.825
103 C[Sn](C)(Cl)Cl 0.9906519 337.000 289.604
104 C[Sn](C)(I)I 0.9979228 150.000 129.593
107 CC(C)(C)[Sn](C)(C)C 1.0007697 67.000 66.942
108 Br[Sn](C)(C)C 0.9987860 138.100 110.598
109 C[Sn](C)(C)C(@O)c1ccccc1 1.0085495 90.400 104.270
110 C[Sn](C)(C)Cl 0.9972577 174.900 144.230
111 C[Sn](C)(C)CC 1.0030661 26.300 16.404
112 C[Sn](C)C 1.0050587 24.900 27.448
113 C[Sn](C)(C)I 1.0009107 82.400 63.838
115 C[Sn](C)(C)CCC 1.0022254 46.800 34.906
116 C[Sn](C)(C)C 1.0039076 17.600 2.114
117 C[Sn](Cl)(Cl)Cl 0.9840899 417.100 434.016
118 C[Sn](c1ccccc1)(c2ccccc2)c3ccccc3 1.0226549 406.000 414.689
119 c1ccc(cc1)[Sn]2(CCCC2)c3ccccc3 1.0148714 301.800 243.396
121 C(#Cc1ccccc1)[Sn](c2ccccc2)(c3ccccc3)c4ccccc4 1.0363099 736.900 715.197
122 C@C[Sn](c1ccccc1)(c2ccccc2)c3ccccc3 1.0261178 528.600 490.897
123 c1ccccc1[Sn](c2ccccc2)(c3ccccc3)c4ccccc4 1.0290499 575.400 555.426
124 CCC[Sn](CCC)(CCC)CCC 0.9971957 142.900 145.594
125 CC(C)CC[Te]CCC(C)C 0.9978520 148.000 131.150
126 CC(C)[Te]C(C)C 1.0012045 46.000 57.371
127 CCCC[Te]CCCC 1.0001418 56.000 80.759
128 CC[Te]CC 1.0035020 4.400 6.811
129 C[Te]C 1.0051863 28.600 30.256
130 CCCCC[Te]CCCCC 0.9984659 137.000 117.641
132 CCC[Te]CCC 1.0018205 42.500 43.816
Test set
3 CC[Al](CC)CC 0.9987272 114.100 111.890
7 c1ccccc1[As](c2ccccc2)c3ccccc3 1.0231112 408.400 424.730
11 ClB(Cl)c1ccc(C)cc1 0.9897731 294.400 308.944
12 CC(CCCCCC)B(C(C)CCCCCC)C(C)CCCCCC 0.9805848 507.900 511.153
17 C1CCCCC1B(C2CCCCC2)C3CCCCC3 0.9870448 397.900 368.986
24 CCCB(CCC)CCC 0.9939128 236.000 217.842
(continued on next page)
A.A. Toropov et al. / Chemical Physics Letters 461 (2008) 343–347 345
Table 3 (continued)
No.a SMILES DCW Exp. Calcd.
27 c1ccccc1[Bi](c2ccccc2)c3ccccc3 1.0329986 600.600 642.325
29 CC(C)C[Ga](CC(C)C)CC(C)C 0.9940045 239.500 215.825
34 CCCC[Ge](CCCC)(CCCC)CCCC 0.9891025 310.000 323.702
39 C[Ge](C)(C)Cl 0.9924874 266.100 249.212
40 C[Ge](C)(C)C 0.9991054 107.500 103.568
52 Br[Hg]C 1.0031593 18.600 14.352
54 CCCC[Hg]CCCC 1.0032432 32.400 12.507
55 O@C([Hg]C(@O)c1ccccc1)c2ccccc2 1.0203788 278.000 364.599
59 CCC[Hg]Cl 0.9999460 88.100 85.068
62 CC[Hg]I 1.0044508 14.300 14.069
68 CC[Pb](CC)(CC)CC 1.0057928 109.600 43.604
74 c1ccccc1[Pb](c2ccccc2)(c3ccccc3)c4ccccc4 1.0344462 674.100 674.183
75 CP(C)C 1.0001024 94.100 81.627
77 CCCC[Sb](CCCC)CCCC 0.9992069 100.500 101.334
79 C[Sb](C)C 1.0067761 32.200 65.243
83 CCCC[Se]CCCC 0.9983144 131.800 120.976
89 C[Si](C)(Cl)Cl 0.9817072 461.100 486.451
105 C[Sn](C)(C)C@C 1.0073070 91.500 76.925
106 CC(C)[Sn](C)(C)C 1.0019172 43.700 41.687
114 C[Sn](C)(C)c1ccccc1 1.0091750 113.600 118.034
120 c1cc(ccc1)[Sn]2(CCCCC2)c3ccccc3 1.0140207 289.000 224.676
131 c1ccccc1[Te]c2ccccc2 1.0184865 306.800 322.954
a The numbering is taken from Ref. [6].
346 A.A. Toropov et al. / Chemical Physics Letters 461 (2008) 343–347R2 = 0.990, s = 32.0 (test set). Molecular graphs are at the basis of
descriptors used in [6]. Thus, the predictive abilities of the
SMILES-based descriptors and regression analysis using descrip-
tors calculated with molecular graphs [6] are very similar.
The criteria for validation of the QSPR/QSAR for the external test
set suggested in Ref. [22] are the following:k ¼
P
½Ye  Yp2
P
Ye2
k0 ¼
P
½Ye  Yp2
P
Yp2Y0e ¼ k  Yp Y0p ¼ k0  YeRo2 ¼ 1
P
½Yp Y0e2
P
½Yp YApÞ2Ro02 ¼ 1
P
½Ye Y0p2
P
½Ye YAeÞ2where Ye and Yp are experimental and predicted values for a test
set; YAe and YAp are average values of the Ye and Yp.
According to Ref. [22], QSPR/QSAR models can be considered
acceptable if they satisfy all the following conditions:
ðR2—Ro2Þ=R2 < 0:1 and ðR2  Ro02Þ=R2 < 0:1;
0:85 < k < 1:15 and 0:85 < k0 < 1:15
An indicator of the external predictability of QSPR/QSAR mod-
els, suggested in Ref. [23], is the following:
Rm2 ¼ R2ð1 absðR2  Ro2Þ0:5Þ > 0:5
These characteristics of the model calculated with Eq. (2) (for
the test set) are the following:
k ¼ 0:97921; k0 ¼ 1:01149;
ðR2  Ro2Þ=R2 ¼ 0:0092 < 0:1
ðR2  Ro02Þ=R2 ¼ 0:0089 < 0:1
Rm2 ¼ 0:8962 > 0:5Thus the Eq. (2) satisfies the criteria suggested in Refs. [22,23].
An important advantage of the suggested approach is the possi-
bility to carry out QSPR/QSAR analyses directly from databases on
physicochemical properties and/or biological activity available via
the internet. Since each SMILES symbol has transparent physico-
chemical interpretation, this approach is able to hint ideas related
to the mechanisms of physicochemical and biological phenomena
which have influence on the property/activity of interest.Acknowledgement
The authors thank the Marie Curie Fellowship for financial sup-
port (the contract ID 39036, CHEMPREDICT).Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.cplett.2008.07.027.References
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QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors

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QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors

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