The structures and the wear data of 47 different organic compounds as lubricant base oils were included in a comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)–quantitative structure tribo-ability relationship (QSTR) model. CoMFA- and CoMSIA-QSTR models illustrate good accuracy, robustness, and predictability, with the latter more accurate than the former. CoMFA-QSTR with both steric and electrostatic fields: R2= 0. 958, R2(LOO) = 0.958, and q2= 0.625; with only a steric field: R2= 0.987, R2(LOO) = 0.987, and q2= 0.692. CoMSIA-QSTR with a steric field: R2= 0.924, R2(LOO) = 0.923, and q2= 0.898, whereas CoMSIA-QSTR with a hydrophobic field gave R2= 0.985, R2(LOO) = 0.985, and q2= 0.899. QSTR with CoMFA and CoMSIA shows a strong correlation to wear scar diameter scales (WDS), and builds statistical and graphical models that relate the wear properties of molecules to their structures.

References

References
1.
Hansch
,
C.
, and
Steward
,
A. R.
,
1964
, “
The Use of Substituent Constants in the Analysis of the Structure–Activity Relationship in Penicillin Derivatives
,”
J. Med. Chem.
,
7
(
6
), pp.
691
694
.
2.
Cramer
, III,
R. D.
,
Patterson
,
D. E.
, and
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
(
18
), pp.
5959
5967
.
3.
Dai
,
K.
, and
Gao
,
X.
,
2013
, “
Estimating Antiwear Properties of Lubricant Additives Using a Quantitative Structure Tribo-Ability Relationship Model With Back Propagation Neural Network
,”
Wear
,
306
(
1–2
), pp.
242
247
.
4.
Gao
,
X.
,
Wang
,
Z.
,
Zhang
,
H.
,
Dai
,
K.
, and
Wang
,
T.
,
2015
, “
A Quantitative Structure Tribo-ability Relationship Model for Ester Lubricant Base Oils
,”
ASME J. Tribol.
,
137
(
2
), p.
021801
.
5.
Gao
,
X.
,
Wang
,
Z.
,
Zhang
,
H.
, and
Dai
,
K.
,
2015
, “
A Three Dimensional Quantitative Tribo-Ability Relationship Model
,”
ASME J. Tribol.
,
137
(
2
), p.
021802
.
6.
Gao
,
X.
,
Wang
,
R.
,
Wang
,
Z.
, and
Dai
,
K.
,
2016
, “
BPNN-QSTR Friction Model for Organic Compounds as Potential Lubricant Base Oils
,”
ASME J. Tribol.
138
(
3
), p.031801.
7.
Mang
,
T.
, and
Dresel
,
W.
,
2001
,
Lubricants and Lubrication
,
Wiley
,
Weinheim, Germany
.
8.
Cramer
, III,
R. D.
,
Patterson
,
D. E.
, and
Bunce
,
J. D.
,
1989
, “
Recent Advances in Comparative Molecular Field Analysis (CoMFA)
,”
Prog. Clin. Biol. Res.
,
291
, pp.
161
165
.
9.
Kubinyi
,
H.
,
Folkers
,
G.
, and
Martin
,
Y. C.
,
1998
,
3D QSAR in Drug Design, Recent Advances
,
Springer
,
The Netherlands
.
10.
Bush
,
B. L.
, and
Nachbar
,
R. B.
,
1993
, “
Sample-Distance Partial Least Squares: PLS Optimized for Many Variables, With Application to CoMFA
,”
J. Comput.-Aided Mol. Des.
,
7
(
5
), pp.
587
619
.
11.
Huang
,
M.
,
Yang
,
D.
,
Shang
,
Z.
,
Zou
,
J.
, and
Yu
,
Q.
,
2002
, “
3D-QSAR Studies on 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors by Comparative Molecular Field Analysis (CoMFA)
,”
Bioorg. Med. Chem. Lett.
,
12
(
17
), pp.
2271
2275
.
12.
Travis
,
R. H.
,
Richard
,
J.
,
Sciotti
,
P. L.
,
Sandra
,
D.
,
Vicky
,
M.
,
Avery
,
O. I.
,
Matthew
,
A.
, and
Timothy
,
J. H.
,
2015
, “
The Synthesis, Antimalarial Activity and CoMFA Analysis of Novel Aminoalkylated Quercetin Analogs
,”
Bioorg. Med. Chem. Lett.
,
25
, pp.
327
332
.
13.
Kubinyi
,
H.
,
2003
, “
Comparative Molecular Field Analysis (CoMFA)
,” in
Handbook of Chemoinformatics: From Data to Knowledge in 4 Volumes
,
J.
Gasteiger
, ed.,
Wiley-VCH Verlag GmbH
,
Weinheim, Germany
, pp.
1555
1574
.
14.
Klebe
,
G.
,
Abraham
,
U.
, and
Mietzner
,
T.
,
1994
, “
Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules Tocorrelate and Predict Their Biological Activity
,”
J. Med. Chem.
,
37
(
24
), pp.
4130
4146
.
15.
Klebe
,
G.
, and
Abraham
,
U.
,
1999
, “
Comparative Molecular Similarity Index Analysis (CoMSIA) to Study Hydrogen-Bonding Properties and to Score Combinatorial Libraries
,”
J. Comput.-Aided Mol. Des.
,
13
(
1
), pp.
1
10
.
16.
Hattotuwagama
,
C. K.
,
Doytchinova
, I
. A.
, and
Flower
,
D. R.
,
2005
, “
In Silico Prediction of Peptide Binding Affinity to Class I Mouse Major Histocompatibility Complexes: A Comparative Molecular Similarity Index Analysis (CoMSIA) Study
,”
J. Chem. Inf. Model.
,
45
(
5
), pp.
1415
1423
.
17.
Arvind
,
K.
,
Solomon
,
K. A.
, and
Rajan
,
S. S.
,
2014
, “
QSAR Studies on Diclofenac Analogues as Potent Cyclooxygenase Inhibitors Using CoMFA and CoMSIA
,”
Med. Chem. Res.
,
23
(
4
), pp.
1789
1796
.
18.
Nilanjan
,
A.
,
Amit
,
K. H.
,
Chanchal
,
M.
, and
Tarun
,
J.
,
2013
, “
Exploring Structural Requirements of Aurone Derivatives as Antimalarials by Validated DFT-Based QSAR, HQSAR, and COMFA–COMSIA Approach
,”
Med. Chem. Res.
,
22
, pp.
6029
6045
.
19.
Bohm
,
M.
,
Sturzebecher
,
J.
, and
Klebe
,
G.
,
1999
, “
Three-Dimensional Quantitative Structure–Activity Relationship Analyses Using Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis To Elucidate Selectivity Differences of Inhibitors Binding to Trypsin, Thrombin, and Factor Xa
,”
J. Med. Chem.
,
42
(
3
), pp.
458
477
.
20.
Bang
,
S. J.
, and
Cho
,
S. J.
,
2004
, “
Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA) Study of Mutagen X
,”
Bull. Korean Chem. Soc.
,
25
(
10
), pp.
1525
1530
.
21.
Clark
,
M.
, and
Cramer
, III,
R. D.
,
1993
, “
The Probability of Chance Correlation Using Partial Least Squares (PLS)
,”
Quant. Struct.–Act. Relat.
,
12
(
2
), pp.
137
145
.
22.
Gao
,
X.
,
Dai
,
K.
,
Wang
,
Z.
,
Wang
,
T.
, and
He
,
J.
,
2016
, “
Establishing Quantitative Structure Tribo-ability Relationship model Using Bayesian Regularization Neural Network
,”
Friction
(accepted).
23.
SYBYL-X 1.1
,
2009
, Tripos International, St. Louis, MO.
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