The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS
By Igor V. Tetko, Daniel M. Lowe, Antony J. Williams. 2016
82 citations recorded by the source corpus.
AbstractBackground Melting point (MP) is an important property in regards to the solubility of chemical compounds. Its prediction from chemical structure remains a highly challenging task for quantitative structure–activity relationship studies. Success in this area of research critically depends on the availability of high quality MP data as well as accurate chemical structure representations in order to develop models. Currently, available datasets for MP predictions have been limited to around 50k molecules while lots more data are routinely generated following the synthesis of novel materials. Significant amounts of MP data are freely available within the patent literature and, if it were available in the appropriate form, could potentially be used to develop predictive models.ResultsWe have developed a pipeline for the automated extraction and annotation of chemical data from published PATENTS. Almost 300,000 data points have been collected and used to develop models to predict melting and pyrolysis (decomposition) points using tools available on the OCHEM modeling platform (http://ochem.eu). A number of technical challenges were simultaneously solved to develop models based on these data. These included the handing of sparse data matrices with >200,000,000,000 entries and parallel calculations using 32 × 6 cores per task using 13 descriptor sets totaling more than 700,000 descriptors. We showed that models developed using data collected from PATENTS had similar or better prediction accuracy compared to the highly curated data used in previous publications. The separation of data for chemicals that decomposed rather than melting, from compounds that did undergo a normal melting transition, was performed and models for both pyrolysis and MPs were developed. The accuracy of the consensus MP models for molecules from the drug-like region of chemical space was similar to their estimated experimental accuracy, 32 °C. Last but not least, important structural features related to the pyrolysis of chemicals were identified, and a model to predict whether a compound will decompose instead of melting was developed.ConclusionsWe have shown that automated tools for the analysis of chemical information have reached a mature stage allowing for the extraction and collection of high quality data to enable the development of structure–activity relationship models. The developed models and data are publicly available at http://ochem.eu/article/99826.
Subfields: Computational Theory and Mathematics, Environmental Chemistry, Materials Chemistry
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In Computational Theory and Mathematics
- Christopher M. Grulke
- Richard S. Judson
- Kamel Mansouri
- Antony J. Williams
- Douglas B. Kell
- David Broadhurst
In Materials Chemistry
Citations in the corpus
- Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds (2015, unknown): “01 Pyrolysis/ Melting point classification [13] Estate (out of 10) 241699 LibSVM 78.”
- OPERA models for predicting physicochemical properties and environmental fate endpoints (2018, methodology): “[143] reports an RMSE of 32 °C for a model built on a dataset of 300,000 chemicals.”