high-throughput computer-assisted identification platform
TRANSCRIPT
Page: 1 / 25 Knorr / 2nd European GCxGC Symposium, 2011
High-Throughput Computer-Assisted Identification Platform
of Small Molecules
ACD/Labs European Users Meeting 2012
June 12-13, 2012
A. Knorr, A. Monge, D. Arndt, E. Martin, and P. Pospisil
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 3 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Aim of CASI
Goal
• To accelerate and standardize the identification of small molecules with highest
confidence possible
• To Increase the throughput of identified compound structures by fully automatic
process
• Standardize the identification process
CASI is Computer-Assisted Structure Identification platform This platform identifies automatically compound structures in highly complex matrices
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 4 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Example
Compound ?
Smoke of a conventional cigarette, measured by GCxGC-TOF
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 5 / 25 Knorr / 2nd European GCxGC Symposium, 2011
CASI Automated Workflow
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 6 / 25 Knorr / 2nd European GCxGC Symposium, 2011
CASI Software Architecture
NIST MS
Search RapidMiner
(Modeling)
Dragon
(Modeling)
CASI Core Engine (Java + Tomcat)
Oracle
11gR2
Oracle Application Express
(Web Application)
ACD PhysChem
ACD Batch
ACD name-to-
structure
SOAP
Hibernate
Query
Mass
Spectra
Pipeline
Pilot
(Chemistry)
MS Databases
(NIST Format)
ACD/Labs + Pipeline Pilot Server
CASI Server
Java API CLJava API
Input data
· JDX file (mass spectra file)
· Experimental KI
· Experimental 2D retention index
CLCL
ORACLE Server
HPC environment
CL: command line
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 9 / 25 Knorr / 2nd European GCxGC Symposium, 2011
CASI Automated Workflow
Latest spectra databases acquired (versions 2010)
> 650’000 Mass Spectra available in CASI
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 10 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
Computational modeling:
Kovats Indices
Several good models were published.
We model Kovats Indices for selected instruments.
GCxGC-TOF 2D retention
Never published.
BP for unknown compounds
It is known that Boiling Point is correlated to Kovats Indices.
BP can be also predicted or structure-retrieved (ACD/Labs).
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 11 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Predictive QSPR Model for KI, 2DrelRT and BP
GA – Support Vector Regression,
8 Molecular Descriptors
Kovats Index 2DrelRT Boiling Point
Linear Regression: BP calc. by
ACD/PhysChem vs. BP calc. by KI
GA – Linear Regression,
15 Molecular Descriptors
Validation r2 = 0.981 r2 = 0.855 r2 = 0.942
Selected models
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 13 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
Experimental method to determine
2D relative retention time (2DrelRT)
Never published
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 14 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Experimental 2D Relative RT Approach for GCxGC-TOF
• Currently, no 2D relative RT approach (2DrelRT) published
• Advantage of having 2DrelRT approach compared to 2D absolute RT (2DabsRT) approach: correction of
systemic GC fluctuations, enhanced reproducibility
• Challenge is the definition of a reference system accessible to all 2D peaks
• Our approach is to refer 2D peaks to hypothetical reference points based on linear regression on deuterated
n-alkanes (patented)
2
nd
dim
ensio
n s
epara
tion t
ime [
seconds]
1st dimension separation time [seconds]
deuterated
n-alkane 1
Example
compound
hypothetical n-alkane retention
(for 1D RT range < 1D RTdA2)
deuterated
n-alkane 2
deuterated
n-alkane 3
2D RThypothetical
n-alkane
abs 2D RTComp
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 15 / 25 Knorr / 2nd European GCxGC Symposium, 2011
2DrelRT for GCxGC-TOF Data - Reproducibility
• Comparison of 3 different studies showed significantly better reproducibility using relative
than absolute 2DRT.
• 1219 compounds found (signal/noise >250), no outlier correction
0
100
200
300
400
0.3
0.6
0.9
1.2
1.5
2.0
2.5
3.0
3.5
4.0
5.0
6.0
7.0
8.0
10.0
>10.0
Relative standard deviation of absolute / relative retention time
for the second dimension of GCxGC-TOF (RSD, %)
Nu
mb
er
of
co
mp
ou
nd
s 2D-relRT
2D-absRT
4.3%2.5%
90th Percentile
(1097 compounds):
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 16 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
Experimental method to determine
In-silico fragmentation tools
Not included in CASI Score
In-silico
fragmentation
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 17 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Evaluation of in-silico fragmentation tools for CASI
• In-silico tool ”AutoAssignment” by ACD/Labs was used to
evaluate the rate of assignment of molecular
substructures to fragments
• Results by “AutoAssignment” for 90 MS of confirmed
structures and the corresponding 50 hits for each proposal
from NIST MS Search (in total 4500 structures)
(AutoAssignment parameters optimized by ACD/Labs)
27%
• Reject approach for low resolution mass-spectrometry by GCxGC-TOF
• Evaluate approach for accurate mass-spectrometry by GC-APCI-TOF (selectivity of substructure
assignment will increase by accurate mass of fragments/isotopic pattern)
Statement from ACD/Labs (Graham McGibbon, ACD/Labs MS Product Manager):
“The AutoAssignment tool was designed primarily to facilitate fragmentation interpretation rather
than for definitive structure differentiation, which currently needs comparisons of specific
characteristic ions and authentic spectra.”
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 18 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
CASI Score is a combination of NIST Match Factor (from step 2)
and of the three components of step 3:
predquerytrainBPBPpredquerytrainDRTDRT
predquerytrainKIKI
BPBPSEPhypDRTDRTSEPhyp
KIKISEPhypNIST MFCASI Score
,,2,2,
,,
22
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 20 / 25 Knorr / 2nd European GCxGC Symposium, 2011
CASI Score function fitting – KI example
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 10.0
Score 0.908
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
8
0 0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 1000.0
Score 1.000
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
5
3
0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 300.0
Score 1.000
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
6
2
0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI
mo
du
le
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 100.0
Score 0.999
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
6
2
0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 50.0
Score 0.996
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
6
2
0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 30.0
Score 0.990
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
7
10 0 0
0
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 20.0
Score 0.977
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
7
10 0 0
0
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 10.0
Score 0.908
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
8
0 0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 5.0
Score 0.630
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
8
0 0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 3.0
Score 0.000
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
7
0 0 01
0
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 2.0
Score 0.000
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
5
10
1 1
0
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 1.0
Score 0.000
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
3
1 10
3
0
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
Predicted KI 2000.0
Experimental KI 2200.0
Standard Error of
Prediction65.8
Curve fitting (variable) 5.0
Score 0.630
Example: 8x50 structures with highest
NIST Fit
- red squares: Score correct structure
- blue dots: Score wrong proposals
from NIST
Score by MS Similarity and
Predicted KI
0
200
400
600
800
1000
0 1 2 3 4 5 6 7 8 9
Example Compound No.
CA
SI
Sc
ore
Good Hits
Hit ranking of correct
structures
8
0 0 0 00
2
4
6
8
10
#1 #2 #3 #4 #5-50
Hit-No.
Visualization of curve fitting
0.0
0.2
0.4
0.6
0.8
1.0
500 1500 2500 3500
Kovats Index
Sc
ore
of
KI m
od
ule
• Training (fitting) of the CASI Score is done over all predicted modules (KI, BP,
2DrelRT) simultaneously and is based on #1-Hits.
• All fittings are done automatically (no manual intervention).
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 21 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
Ranking of suggested hits
by CASI score
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 22 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Analysis page example
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Nr. 1
Nr. 2
Nr. 3
Nr. 4 Nr. 1
Nr. 2
Nr. 3
Nr. 4
CASI NIST
Page: 24 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Performance Indicators of CASI Platform
Discrimination between identified and unknowns:
CASI score < 795:
Pospisil et al, ACD/Labs European Users’ Meeting 2012
0
10
20
30
40
50
300 400 500 600 700 800 900 1000
CASI Score
Fre
qu
en
cy
Correct hits of the validation set
Selected hit of unknown compounds
0
10
20
30
40
50
300 400 500 600 700 800 900 1000
NIST MS Search Match Factor
Fre
qu
en
cy
Correct hits of the validation set
Hit with highest score for unknowns
NIST score < 825:
Distribution of the CASI scores or NIST Match Factors for the correct hits (blue) of the validation set and of the hits of
unknown compounds selected by default (1st highest score, pink) for a set of 176 unknown compounds
Below this threshold: high probability to be true negatives (false proposals given by mass-spectral
databases)
Page: 26 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Dataset for CASI Platform
• Comparing the chemical composition of different smoke samples
• Application of non-targeted differential screening using GCxGC-EI-TOF-MS
• GCxGC-EI-TOF-MS data provide:
– structural proposals for the most relevant differences
– unknowns (no structural proposal available)
• 218 structural proposals were confirmed by reference standards
• 176 unknowns were additionally included in dataset
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 27 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Range and Structural Diversity of CASI dataset
Comparison of CASI dataset against
smoke related organic compounds (>6000)
CASI compounds ( ) are distributed
between a broad range of structural features of
the in-house database ( ) of smoke related
organic compounds.
- PCA based on ECFP6 fingerprints: circular topological fingerprints
for e.g. similarity searching Dataset we used covers the common ranges
of GC-MS
50
150
250
350
450
550
500 1500 2500 3500
Kovats Index
Mo
lec
ula
r w
eig
ht
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 28 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Overall results for current dataset (Identified + Unknowns)
• Evaluation of results on combined dataset (247 mass-spectra) by contingency table
Used threshold of being identified: 825 for NIST MS Search, 795 for CASI Score)
• CASI Platform shows significantly less false positives (11 vs. 29) than NIST MS search.
true
(CASI score)
false
(CASI score)
true
(NIST MF)
false
(NIST MF)
positive 46 11 40 29
negative 165 14 147 20
total (%) 89% 11% 79% 21%
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 29 / 25 Knorr / 2nd European GCxGC Symposium, 2011
1
GCxGC-TOF
2
Search in Mass Spectra
Databases
(NIST MS Search)
2nd
column
relative retention
time matching
KI matching
Multi JDX MS file
+ KI experimental values
+ relative second retention time
Hits
Boiling Point
matching
3 CASI Score
4
Ranking
5
Submission to
UCSD
database
Sorted Hits Confirmed Hits
CASI Automated Workflow
Automatic submission into PMI
unique chemical database
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 30 / 25 Knorr / 2nd European GCxGC Symposium, 2011
User Web Interface
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 31 / 25 Knorr / 2nd European GCxGC Symposium, 2011
User and admin web interface
Job queueJob queueResult summaryResults summaryResult summaryResults summaryCandidates for each resultCandidates for each queryCandidates for each resultCandidates for each query
Export to
.sdf or
publish in in-
house
database
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 32 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Conclusion
• CASI (for GC) platform is functioning.
• CASI can be expanded to other GC systems with the possibility to
dynamically change it to other instruments and analytical columns.
• Innovative 2D relative retention time concept is proven working.
• Robust models for KI, BP and 2DrelRT were established.
• CASI Score performs better than NIST MS Search.
• Patent application submitted (>20 claims).
• Very good feedback from the scientific community.
• Platform will be extended to accurate mass and LC-MS and to other GC
systems
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 34 / 25 Knorr / 2nd European GCxGC Symposium, 2011
Acknowledgment
Bioanalytical chemistry
Chemoinformatics
Arno Knorr Markus Stueber
Andre Stratmann Daniel Arndt Manuel Peitsch Pavel Pospisil
Elyette Martin Aurelien Monge
Pospisil et al, ACD/Labs European Users’ Meeting 2012
Page: 35 / 25 Knorr / 2nd European GCxGC Symposium, 2011
End
Thank you for your attention.
Pospisil et al, ACD/Labs European Users’ Meeting 2012