1. Understanding And
Classifying Metabolite
Space and Metabolite-
Likeness
PLoS One (in press)
Julio E. Peironcely  @peyron
Juliopeironcely.com
PhD student at Leiden University and TNO

2. Metabolomics
the quantitative and qualitative
analysis of all metabolites in
samples of cells, body fluids,
tissues, etc.
Julio E. Peironcely

3. Metabolomics
Experi- Biological
Biological Sample Data Data pre- Data
mental Sampling inter-
question preparation acquisition processing analysis
design pretation
Metabolites
Relevant
biomolecules/
List of
Samples Raw data connectivities
Protocol peaks/
&
biomolecules
Models
Julio E. Peironcely

4. Metabolomics
Experi- Biological
Biological Sample Data Data pre- Data
mental Sampling inter-
question preparation acquisition processing analysis
design pretation
Metabolites
Relevant
biomolecules/
List of
Samples Raw data connectivities
Protocol peaks/
&
biomolecules
Models
Julio E. Peironcely

5. How do metabolites
look like?

6. HMDB ZINC
8K 21M
Julio E. Peironcely

7. metabolites non metabolites
Water Solubility
MW
C Atoms
Struc. Complexity
PSA
Julio E. Peironcely

8. PCA
Julio E. Peironcely

9. PCA

10. Not so different

11. Decision Tree
Julio E. Peironcely

12. Lots of candidates
structures

13. Elemental
Composition
Julio E. Peironcely

14. Elemental
Composition
Structure
Generation
Julio E. Peironcely

15. Elemental
Composition
Structure
Generation
Molecules
Julio E. Peironcely

16. We are looking for
metabolites

17. Elemental
Composition
Structure Metabolite
Generation Likeness
Molecules
Julio E. Peironcely

18. Elemental
Composition
Metabolites
Structure Metabolite
Generation Likeness
Molecules
Julio E. Peironcely

19. Metabolite-likeness
Representation + Classification
HMDB ZINC
8K 21M
Atom Counts
Physicochemical desc. Support Vector
Machines (SVM)
MDL Public Keys
Random Forest (RF)
FCFP_4
Naïve Bayes (NB)
ECFP_4
Julio E. Peironcely

20. Metabolite-likeness HMDB
8K
ZINC
21M
Standardization
Atom Counts Diversity Selection
Physicochemical desc.
MDL Public Keys
FCFP_4
ECFP_4
Julio E. Peironcely

21. Metabolite-likeness HMDB
8K
ZINC
21M
Standardization
Atom Counts Diversity Selection
Physicochemical desc.
MDL Public Keys
FCFP_4 Training Set Test Set
ECFP_4 532 + 532 6.4K + 6.4K
Julio E. Peironcely

22. Metabolite-likeness HMDB
8K
ZINC
21M
Standardization
Atom Counts Diversity Selection
Physicochemical desc.
MDL Public Keys
FCFP_4 Training Set Test Set
ECFP_4 532 + 532 6.4K + 6.4K
5-fold CV
SVM RF BC
Julio E. Peironcely

23. Metabolite-likeness HMDB
8K
ZINC
21M
Standardization
Diversity Selection
3 classifiers
X
Training Set Test Set
5 descriptions 532 + 532 6.4K + 6.4K
5-fold CV Metabolite
likeness
SVM RF BC
Julio E. Peironcely

24. Metabolite-likeness HMDB
8K
ZINC
21M
Best = RF – MDLPublicKeys Standardization
Sensitivity Specificity AUC
Diversity Selection
99.84% 87.52% 99.20%
Training Set Test Set
Bad BC – P_desc 532 + 532 6.4K + 6.4K
Sensitivity Specificity AUC 5-fold CV Metabolite
likeness
SVM RF BC
42.51% 86.56% 61.57%
Julio E. Peironcely

25. Metabolite-likeness, external
validation
HMDB
External DrugBank ChEMBL
validation set
Random Selection
Standardization
Metabolite
likeness
Julio E. Peironcely

26. Metabolite-likeness, external
validation
Julio E. Peironcely

28. Met-likeness + structure generation
(methylhistamine) 260K
71%
46%
Julio E. Peironcely

29. Met-likeness + structure generation
(malic acid) 8K
100%
57% 77%
Julio E. Peironcely

30. Conclusions
Prediction is good, interpretation not
Useful in different fields
Local models needed
Julio E. Peironcely

31. Acknowledgements
Leiden University University of Cambridge
Theo Reijmers Andreas Bender
Thomas Hankemeier
TNO Quality of Life HMP University of
Alberta
Leon Coulier
David Wishart
Ying (Edison) Dong
Julio E. Peironcely