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Jean-Claude Bradley and Andrew Lang present at the 5th Meeting on U.S. Government Chemical Databases and Open Chemistry on August 26, 2011 about "The collection, curation and modeling of Open Melting Point measurements". The talk also covers the role of Open Notebook Science and Google Apps Scripts in this effort.
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The collection, curation and modeling of Open Melting Point measurements
August 26, 2011
5th Meeting on U.S. Government Chemical Databases and Open Chemistry
Jean-Claude Bradley
Department of ChemistryDrexel University
Andrew Lang
Department of MathematicsOral Roberts University
Antony Williams
ChemSpiderRoyal Society of
Chemistry
The Problem of Data Quality in Chemistry
• Lack of provenance
•Reliance on a system of “trusted sources”
• CRC Handbook•Merck Index• Chemical Vendor Catalogs (e.g. Sigma-Aldrich)• Peer-Reviewed Journals
In the case of melting points:
Strategy for the curation of melting points
Using technology, we can begin to replace the “trusted source”
model with one based on transparency and provenance
1. Rely on redundancy when possible2. Provide the maximum level of
provenance when necessary (Open Notebook Science)
3. Adhere to Open Data, Open Descriptors and Open Algorithms for measurements and modeling
The Chemical Information Validation Sheet
567 curated and referenced measurements from Fall 2010 Chemical Information Retrieval course
Investigating the m.p. inconsistencies of EGCG
Most popular data sources
Alfa Aesar donates melting points to the public
Open Melting Point Explorer
OutliersMDPI
datasetEPA/PhysProp
(donated all data to public also)
Outliers for ethanol: Alfa Aesar and Oxford MSDS
Inconsistencies and SMILES problems within MDPI dataset
MDPI Dataset labeled with High Trust Level
EPA/PHYSPROP Structure Errors (Incorrect Valence): 2315 out of 43543 were contained pentavalent
nitrogens
EPA/PHYSPROP Errors: Structure displayed is for the neutral compound dopamine but the associated CAS
Number and chemical name in the file are for the hydrobromide salt.
Common errors in datasets
1. multiple melting points for the same compound in the same database
2. stereochemistry issues3. sign inversion4. conversion errors (Kelvin/Celcius
Fahrenheit/Celcius)5. bad SMILES (non-rendering)6. salts associated with SMILES for free base7. using boiling point for melting point
Open melting point datasets
Double+ validated: 2706 compounds (7413 highly curated measurements. range: 0.01-5 C. Compounds that had at least one chiral center, possessed cis/trans isomerism, were inorganic or a salt removed.)
Entire dataset: 19933 unique compounds (27684 measurements – no inorganics or salts)
Open Models with Open Data Using Open Descriptors (CDK)
Modeling Results
Model Training set Test set (TS) Descriptors TS AAE TS RMSE TS R2
1 2205 500 132 2D 29.51 40.91 0.82
1 2204 500 170 2D/3D 29.52 40.79 0.83
2 16015 500 137 2D 26.62 36.35 0.86
3 16015 3500 137 2D 29.36 40.18 0.81
Melting point prediction service
Melting point predictions and measurements on iPhone/iPad (Alex Clark)
Publication of double+ validated melting point dataset to Nature Precedings and LuLu
For all Formats of ONS Projects
Open Melting Point DatasetsCurrently 20,000 compounds with Open MPs
Some melting points can’t be resolved only with literature: 4-benzyltoluene
Motivation: Faster Science, Better Science
Open Lab Notebook page measuring the melting point of 4-benzyltoluene
Using melting point for temperature dependent solubility prediction
Crowdsourcing Solubility Data
Integration of Multiple Web Services to Recommend Solvents for Reactions
All ONS web services
Google Apps Scripts web services
Google Apps Scripts for conveniently exploring melting point data
Straight chain carboxylic acids from 1 to 10 carbons
Straight chain alcohols from 1 to 10 carbons
Comparison of model with triple validated measurements
Cyclic primary amines from 3 to 6 carbons (cyclobutylamine flagged for validation – only single
source available)
Google Apps Scripts for planning reactions and creating schemes
Open Melting Points in Supplementary Data Pages of Wikipedia (Martin Walker)
Conclusions
• For science to progress quickly there is great benefit in moving away from a “trusted source” model to one based on transparency and data provenance
•Open Notebook Science offers an efficient way to make research transparent and discoverable