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Chemical Structure| 20445-88-9

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Francisco Fuentes ; Jonathan Heidema ; Saravanan Ramasamy ; Scott Williams ;

Abstract: Zinc dialkyldithiophosphates (ZDDPs) are widely used anti-wear oil additives that have been shown to reduce the conversion efficiencies of catalytic converters in automobiles and to harm the environment in general. Ionic liquids (ILs) are one type of alternative anti-wear additives that have been widely researched in recent years. Among the ILs of interest is tributylmethylphosphonium dimethylphosphate (PP), which has been found to have superior friction-reducing properties as compared to ZDDP under some conditions. We have heated ZDDP and PP samples for various lengths of time to temperatures up to 200 ˚C. The thermal degradations have been compared by utilizing 31P nuclear magnetic resonance techniques. The results suggest that PP may perform better as a friction-modifier than ZDDP in some situations involving high temperatures.

Purchased from AmBeed:

Francisco Fuentes ; Jonathan Heidema1 ; Saravanan Ramasamy ; Scott Williams ;

Abstract: Zinc dialkyldithiophosphates (ZDDPs) are widely used anti-wear oil additives that have been shown to reduce the conversion efficiencies of catalytic converters in automobiles and to harm the environment in general. Ionic liquids (ILs) are one type of alternative anti-wear additives that have been widely researched in recent years. Among the ILs of interest is tributylmethylphosphonium dimethylphosphate (PP), which has been found to have superior friction-reducing properties as compared to ZDDP under some conditions. We have heated ZDDP and PP samples for various lengths of time to temperatures up to 200 ˚C. The thermal degradations have been compared by utilizing 31P nuclear magnetic resonance techniques. The results suggest that PP may perform better as a friction-modifier than ZDDP in some situations involving high temperatures.

Keywords: tributylmethylphosphonium dimethylphosphate ; zinc dialkyldithiophosphate (ZDDP) ; 31P nuclear magnetic resonance (31P NMR) ; thermal decomposition ; ionic liquids (ILs)

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Alternative Products

Product Details of [ 20445-88-9 ]

CAS No. :20445-88-9
Formula : C15H36O4P2
M.W : 342.39
SMILES Code : C[P+](CCCC)(CCCC)CCCC.O=P(OC)([O-])OC
MDL No. :MFCD22377743
InChI Key :LSDYBCGXPCFFNM-UHFFFAOYSA-M
Pubchem ID :18954875

Safety of [ 20445-88-9 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H315-H319-H335-H412
Precautionary Statements:P261-P273-P305+P351+P338

Computational Chemistry of [ 20445-88-9 ] Show Less

Physicochemical Properties

Num. heavy atoms 21
Num. arom. heavy atoms 0
Fraction Csp3 1.0
Num. rotatable bonds 11
Num. H-bond acceptors 4.0
Num. H-bond donors 0.0
Molar Refractivity 95.83
TPSA ?

Topological Polar Surface Area: Calculated from
Ertl P. et al. 2000 J. Med. Chem.

81.99 Ų

Lipophilicity

Log Po/w (iLOGP)?

iLOGP: in-house physics-based method implemented from
Daina A et al. 2014 J. Chem. Inf. Model.

-0.49
Log Po/w (XLOGP3)?

XLOGP3: Atomistic and knowledge-based method calculated by
XLOGP program, version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry

2.69
Log Po/w (WLOGP)?

WLOGP: Atomistic method implemented from
Wildman SA and Crippen GM. 1999 J. Chem. Inf. Model.

5.85
Log Po/w (MLOGP)?

MLOGP: Topological method implemented from
Moriguchi I. et al. 1992 Chem. Pharm. Bull.
Moriguchi I. et al. 1994 Chem. Pharm. Bull.
Lipinski PA. et al. 2001 Adv. Drug. Deliv. Rev.

3.03
Log Po/w (SILICOS-IT)?

SILICOS-IT: Hybrid fragmental/topological method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

4.33
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

3.08

Water Solubility

Log S (ESOL):?

ESOL: Topological method implemented from
Delaney JS. 2004 J. Chem. Inf. Model.

-2.93
Solubility 0.401 mg/ml ; 0.00117 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Soluble
Log S (Ali)?

Ali: Topological method implemented from
Ali J. et al. 2012 J. Chem. Inf. Model.

-4.06
Solubility 0.0295 mg/ml ; 0.0000862 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Moderately soluble
Log S (SILICOS-IT)?

SILICOS-IT: Fragmental method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

-5.19
Solubility 0.0022 mg/ml ; 0.00000641 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Moderately soluble

Pharmacokinetics

GI absorption?

Gatrointestinal absorption: according to the white of the BOILED-Egg

High
BBB permeant?

BBB permeation: according to the yolk of the BOILED-Egg

No
P-gp substrate?

P-glycoprotein substrate: SVM model built on 1033 molecules (training set)
and tested on 415 molecules (test set)
10-fold CV: ACC=0.72 / AUC=0.77
External: ACC=0.88 / AUC=0.94

Yes
CYP1A2 inhibitor?

Cytochrome P450 1A2 inhibitor: SVM model built on 9145 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.83 / AUC=0.90
External: ACC=0.84 / AUC=0.91

No
CYP2C19 inhibitor?

Cytochrome P450 2C19 inhibitor: SVM model built on 9272 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.80 / AUC=0.86
External: ACC=0.80 / AUC=0.87

No
CYP2C9 inhibitor?

Cytochrome P450 2C9 inhibitor: SVM model built on 5940 molecules (training set)
and tested on 2075 molecules (test set)
10-fold CV: ACC=0.78 / AUC=0.85
External: ACC=0.71 / AUC=0.81

No
CYP2D6 inhibitor?

Cytochrome P450 2D6 inhibitor: SVM model built on 3664 molecules (training set)
and tested on 1068 molecules (test set)
10-fold CV: ACC=0.79 / AUC=0.85
External: ACC=0.81 / AUC=0.87

No
CYP3A4 inhibitor?

Cytochrome P450 3A4 inhibitor: SVM model built on 7518 molecules (training set)
and tested on 2579 molecules (test set)
10-fold CV: ACC=0.77 / AUC=0.85
External: ACC=0.78 / AUC=0.86

Yes
Log Kp (skin permeation)?

Skin permeation: QSPR model implemented from
Potts RO and Guy RH. 1992 Pharm. Res.

-6.48 cm/s

Druglikeness

Lipinski?

Lipinski (Pfizer) filter: implemented from
Lipinski CA. et al. 2001 Adv. Drug Deliv. Rev.
MW ≤ 500
MLOGP ≤ 4.15
N or O ≤ 10
NH or OH ≤ 5

0.0
Ghose?

Ghose filter: implemented from
Ghose AK. et al. 1999 J. Comb. Chem.
160 ≤ MW ≤ 480
-0.4 ≤ WLOGP ≤ 5.6
40 ≤ MR ≤ 130
20 ≤ atoms ≤ 70

None
Veber?

Veber (GSK) filter: implemented from
Veber DF. et al. 2002 J. Med. Chem.
Rotatable bonds ≤ 10
TPSA ≤ 140

1.0
Egan?

Egan (Pharmacia) filter: implemented from
Egan WJ. et al. 2000 J. Med. Chem.
WLOGP ≤ 5.88
TPSA ≤ 131.6

0.0
Muegge?

Muegge (Bayer) filter: implemented from
Muegge I. et al. 2001 J. Med. Chem.
200 ≤ MW ≤ 600
-2 ≤ XLOGP ≤ 5
TPSA ≤ 150
Num. rings ≤ 7
Num. carbon > 4
Num. heteroatoms > 1
Num. rotatable bonds ≤ 15
H-bond acc. ≤ 10
H-bond don. ≤ 5

0.0
Bioavailability Score?

Abbott Bioavailability Score: Probability of F > 10% in rat
implemented from
Martin YC. 2005 J. Med. Chem.

0.55

Medicinal Chemistry

PAINS?

Pan Assay Interference Structures: implemented from
Baell JB. & Holloway GA. 2010 J. Med. Chem.

0.0 alert
Brenk?

Structural Alert: implemented from
Brenk R. et al. 2008 ChemMedChem

1.0 alert: heavy_metal
Leadlikeness?

Leadlikeness: implemented from
Teague SJ. 1999 Angew. Chem. Int. Ed.
250 ≤ MW ≤ 350
XLOGP ≤ 3.5
Num. rotatable bonds ≤ 7

No; 1 violation:MW<1.0
Synthetic accessibility?

Synthetic accessibility score: from 1 (very easy) to 10 (very difficult)
based on 1024 fragmental contributions (FP2) modulated by size and complexity penaties,
trained on 12'782'590 molecules and tested on 40 external molecules (r2 = 0.94)

4.81
 

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