OpenPHACTS - Chemistry Platform Update and Learnings
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This document discusses the Chemical Validation and Standardization Platform (CVSP) system. It provides an overview of the CVSP subsystems and capabilities for validating chemical structures, standardizing structures according to common rules, and linking structured databases. Examples of standardization results and issues found in validation of DrugBank and ChEMBL databases are also presented. The document advocates for defining a common community rule set for chemical standardization across different tools and databases.
![Standardization SMIRKS
Examples of InChI normalization
[*;H+:1]>>[*;H:1]
[O,S,Se,Te:1]=[O+,S+,Se+,Te+:2][C-;v3:3]>>[O,S,Se,Te:1]=[O,S,Se,Te:2]=[C:3]
[N-,P-,As-,Sb-:1]=[C+;v3:2]>>[N,P,As,Sb:1]#[C:2]
Examples of FDA SRS rules
[n:1]=[O:2]>>[n+:1][O-:2]
[*:1]=[N:2]#[N:3]>>[*:1]=[N+:2]=[N-:3]
[N+0;H3:1].[C:3](=[O:4])[O:5][H:6]>>[N+1;H4:1].[C:3](=[O:4])[O-:5]
Thiopurine [H:1][S:2][c:3]1[n:8][c:7]([H,*:13])[n:6][c:5]2[c:4]1[n:11][c:10]
([H,*:12])[n:9]2>>[H:1][N:8]1[C:7]([H,*:13])=[N:6][C:5]2=[C:4]([N:11]=[C:10]
([H,*:12])[N:9]2)[C:3]1=[S:2]](https://image.slidesharecdn.com/cvsppresentationoctober2016final-161025032355/85/OpenPHACTS-Chemistry-Platform-Update-and-Learnings-24-320.jpg)
OpenPHACTS - Chemistry Platform Update and Learnings
- 1. Open PHACTS - Chemistry Platform Update and learnings Antony Williams and Valery Tkachenko ORCID ID:0000-0002-2668-4821
- 2. @gray_alasdair Big Data Integration 2 OpenPHACTS and CRS Diagram
- 3. The Chemical Registration Service Chemistry processing •Validation •Standardization •Properties generation •Properties retrieval Export •RDF •SDF API •Domain-specific searches •Chemical visualization •Properties •Conversions
- 5. Subsystems • “CVSP” (frontend, backend, database) • Compounds (frontend, database) • OpenPHACTS API (frontend, database) • Datasources registry (frontend, database) • Processing farm (optional)
- 6. Structure-Based Database linking • Open PHACTS, and many other projects requiring the linking of structure databases, depend on mappings • Different databases use different processes for standardization prior at deposition • Examples: PubChem, EBI databases, ChemSpider, etc.
- 7. DrugBank • ~60 records can’t be dearomatized unambiguously • ~40 records where InChIs did not match structure • 2 records where SMILES, InChI and name did not match the structure • 7 records with 2 stereo bonds at chiral atoms DB04283 DB04462
- 8. Standardizers • EBI Standardizer: https://wwwdev.ebi.ac.uk/chembl/extra/francis/sta / • PubChem Standardizer: https:// pubchem.ncbi.nlm.nih.gov/standardize/standardi • NCGC Standardizer: https://tripod.nih.gov/? p=61 • The CVSP Standardizer work in Open PHACTS http://cvsp.chemspider.com/
- 10. Standardization Rules • Available from: http://tinyurl.com/hwapem3 • Use the SRS as guidance for standardization • Adjust as necessary to our needs
- 11. Nitro groups
- 12. Salt and Ionic Bonds
- 14. Supports various file formats
- 15. Comptox Chemistry Dashboard Prior to deposition check a deposition…
- 16. >3450 compounds in one SDF
- 17. 98 Errors, 1571 Warnings
- 18. Review Errors
- 20. Various Rules Sets Available
- 21. CVSP – My own custom rules
- 22. ChEMBL Validation Review (of 1.3 million records) • 11,020 records with 4 bonds and zero charge, e.g. CHEMBL501101 or CHEMBL501973 • 271 records with hypervalent oxygen (e.g. , CHEMBL2219679), carbon (e.g. 1005895), boron, chlorine, iodine or phosphine • 6,177 records where direction of bond makes no sense, e.g. CHEMBL12760 and CHEMBL34704
- 23. Chemical Validation first… Standardization Second • Chemical Validation detects errors – Standardization FIXES them according to rules • SMIRKS transformations are based on both InChI Normalization and FDA SRS rules
- 24. Standardization SMIRKS Examples of InChI normalization [*;H+:1]>>[*;H:1] [O,S,Se,Te:1]=[O+,S+,Se+,Te+:2][C-;v3:3]>>[O,S,Se,Te:1]=[O,S,Se,Te:2]=[C:3] [N-,P-,As-,Sb-:1]=[C+;v3:2]>>[N,P,As,Sb:1]#[C:2] Examples of FDA SRS rules [n:1]=[O:2]>>[n+:1][O-:2] [*:1]=[N:2]#[N:3]>>[*:1]=[N+:2]=[N-:3] [N+0;H3:1].[C:3](=[O:4])[O:5][H:6]>>[N+1;H4:1].[C:3](=[O:4])[O-:5] Thiopurine [H:1][S:2][c:3]1[n:8][c:7]([H,*:13])[n:6][c:5]2[c:4]1[n:11][c:10] ([H,*:12])[n:9]2>>[H:1][N:8]1[C:7]([H,*:13])=[N:6][C:5]2=[C:4]([N:11]=[C:10] ([H,*:12])[N:9]2)[C:3]1=[S:2]
- 25. Examples of Standardization Double bond with adjacent wiggly single bond Collapser hydrogen atoms with no stereo bonds
- 26. Examples of Standardization Remove symmetric stereocenters Turn off chiral flag if no up or down bonds
- 27. Defining a Community Rule Set • There are multiple standardizers, each with their own rules set • Can we decide on a default community rules set, like Standard InChI, that could be used by ALL Standardizers? • A joint meeting between the Research Data Alliance (RDA), IUPAC and ACS Division of Chemical Information discussed the value and possibilities of this approach (July 2016)
- 28. EPA is investigating CVSP • EPA is investigating CVSP as a validation and standardization platform • Considering the API aspects of CVSP to integrate to our registration system • CVSP is a reference implementation and “starting point” for a community rules set
- 29. CVSP code is now Open Source • Open Source CVSP code now released • Code is hosted on Open PHACTS Github https://github.com/openphacts/ops-crs • Valery Tkachenko will offer future support • Hoping for additional community engagement and support • Some details of availability….
- 30. Virtual Machines • OPS_FRONT (all websites and API) • OPS_BACK (all heavy-lifting) • OPS_DB (databases) • VMs are VMware images • Can be converted to other hypervisors
- 31. Thank you Emails: tony27587@gmail.com and tkachenko.valery@gmail.com SLIDES: www.slideshare.net/AntonyWilliams
Editor's Notes
- #3: Open PHACTS was developed to support the key questions of drug discovery Business questions have been at the heart of Open PHACTS and have driven the development of the platform Mx/psa, how calculated who did it? Mash up. With your data too, - top layer join together but need them all commercial Data provided by many publishers Originally in many formats: relational, SD files and RDF Worked closely with publishers Data licensing was a major issue Over 5 billion triples – 14 datasets & growing Hosted on beefy hardware; data in memory (aim) Extensive memcaching Pose complex queries to extract data