Understanding And Classifying Metabolite Space and Metabolite-Likeness
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Understanding And Classifying Metabolite Space and Metabolite-Likeness
- 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