Prediction of NMR Chemical Shifts.
A Chemometrical Approach
К.А. Blinov, Y.D. Smurnyy, Т.S. Churanova, М.Е. Elyashberg
Advanced Chemistry Development (ACD)
Structure and its spectral data
COSY.esp
4 3 2 1F2 Chemical Shift (ppm)
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
F1
Che
mic
al S
hift
(ppm
)
HMQC.esp
4 3 2F2 Chemical Shift (ppm)
16
24
32
40
48
56
64
72
80
F1
Che
mic
al S
hift
(ppm
)
C13.esp
85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10Chemical Shift (ppm)
0.25
0.50
0.75
1.00
Nor
mal
ized
Inte
nsity
26.8
531.6
1
42.4
642
.86
48.2
2
50.3
251
.94
52.6
7
60.1
060
.1864
.59
76.7
877
.03
77.2
977
.60
H1.esp
4.0 3.5 3.0 2.5 2.0 1.5Chemical Shift (ppm)
0.25
0.50
0.75
1.00
Nor
mal
ized
Inte
nsity
CH4
StructureSpectraN
NO
O N
NO
O
O
N
N
O
O
N
N
O
Sometimes solution is not obvious
• In many cases we obtain several structures corresponding to spectral data.
• In this case we need a method to rank the structures.
• Most powerful method - compare experimental and predicted 13C NMR spectra
13C NMR spectral data
NN
O
O
N
N
O
O
2,00
9.62
Experimental
Predicted
How to find the best structure?
• In most cases predicted spectrum of “correct structure” has best fit to experimental spectrum
• In practice “correct structure” has average deviation between predicted and experimental spectra 2-3 ppm
The role of the spectra prediction
• Real-world task. Unknown structure with MF C29H32N2O5 and spectral data (1D and 2D NMR).
• 20 min to generate all structures (> 12 000) • 24 hours to predict the NMR 13С spectra
of all the obtained structures• Speed of spectra prediction should be
increased
Methods of the prediction of NMR spectra
• Quantum Mechanics• Database approach
– HOSE Codes– Maximum Common
Substructure
• Rule-based – Additive scheme– Neural Networks
– extremely slow– accurate but slow
– fast but inaccurate
• Our choice – improve accuracy of fast method
Additive scheme
aixi
=
C
O
CH3
C
CH2
CH
CH2
CH2
CH2
153.71-1.85-4.49-1.39-2.79+1.43+0.52+0.52-1.35 = 144.31
153.71
-1.85
-4.49
-1.39
-2.79
1.43
0.52
0.52
-1.35
144.31
Main problem – find correct values of atom increments
Available data
• We have database of 1.5 millions of chemical shifts for 13С.
• We can try to obtain correct values!
How to encode atom environment
CH2Atom’s type
Number of atoms…1 1
CH
Input variables
C
O
CH3
C
CH2
CH
CH2
CH2
CH2
…C
1
1st sphere
CH2 CH3O
2 1 1
2nd sphere
Data for PLS regressionAtom environment encoding
Sam
ples
Chemical shifts
X Y
Find best structure encoding
• Initially best scheme of structure representation does not evident
• We should find scheme which has best accuracy
• We should optimize– substitutents coding scheme – number of used “spheres”
Used data
• 210 K of chemical shifts used as a training set.
• 170 K of chemical shifts from recent literature used as external validation set.
How to describe atom type
• Atom type (C, O, etc.).
• Hybridization (sp3, sp2, etc).
• Valence
• Number of neighbor H.
• Charge
• Distance to “central” atom (bonds)
CH3
CH
CH
NH2
“Central” atom
“Substitutent”
7 (N)
1 (sp3)
32
0
3
Result for different atom encoding
7.17
10.96
5.36
8.76
4.39
6.57
3.52
5.37
0.00
2.00
4.00
6.00
8.00
10.00
12.00
Atoms only + Elementtype
+Hybridization
+ All other
AverageDeviation
StandardDeviation
Result for number of spheres
5.43
7.69
3.97
5.88
3.66
5.51
3.52
5.37
3.51
5.37
3.53
5.40
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
1 2 3 4 5 6
Number of "Spheres"
Averagedeviation
Standarddeviation
Is it the best possible accuracy?
• Best possible average deviation is 3.5 ppm.
• We need less than 3 ppm (2 is preferable).
• Should we use additional variables?
• We should be very careful adding variables.
CH2 C
CH3
CH3
141,48125,90CH2 C
Cl
Cl
CH2 C
Cl
CH3
138,30
125,38CH2 C
H
Cl
Substitutents interference (cross effect)
CH2 C
H
H +2,48
122,90 CH2 C
H
CH3
134,16
+1.34 -1.94 -3.94
145.42127.86136.64
+11,26
C
O
CH3
C
CH2
CH
CH2
CH2
CH2
Enhanced structure encoding
CH2 and CH Atom pair type
Number of pairs…1
Input variables
…
1
Atoms Pairs of atoms (Crosses)
C and O
1 2 3 4
43
21
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
Result for atom pairs (crosses)
Distance between atoms
within a crossNumber of spheres
Mea
n er
ror,
ppm
More enhancements?
• Now accuracy is good enough (2.3 ppm)
• But it is still bad in some cases
• Unfortunately these cases are very important
• This “special” cases should be taken into account
Stereo effects: double bonds
CH3
OOH
CH3
CH3
CH3
25.7
17.6
3,9 A
2,9 A
• We use “topological” distance
• Sometimes equal topological distance correspond to different “real” distances
Modified structure encoding
Atoms Pairs of atoms (Crosses) “Stereo” effects
Variables
Prediction of spectra by different methods (mean error, ppm)
Taken into the account All types of atoms
CH3 =C
Atoms only 3,52 1,55 8,03
+ pairs of atoms (crosses)
2,32 1,50 3,22
+ “stereo” effects 2,27 1,24 3,22
+ solvent 2,25 1,24 3,20
+ to be continued?
Size of training set
• We have 1.5 millions of chemical shifts
• We should try to use all available data
• Only one problem – matrix size
• In many cases matrix size becomes more than 2 GB
Bigger dataset – smaller mean error!
0.00
1.00
2.00
3.00
4.00
5.00
1 2 4 8 16 32 64 128 207
Number of structures in training set (thousands)
Av
era
ge
de
via
tio
n (
pp
m)
The final results
Method Average deviation
The rate of calculationshifts/sec.
Old Method - HOSE Codes
1.87 6
New Additive scheme
1.83 5800
Faster by 3 order!
Prediction time: the past and present
NH
NH
O
O
CH3
CH3
OO
O
CH3
Method Average deviation Time
HOSE Codes 1.72 > 24 hours
Additive scheme 1.63 2 min.
C29H32N2O5
Conclusions
• Combination of “new” method with old well-known algorithm can produce very good (and unexpected) result