ArticleJournal of cheminformatics2016
The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS.
Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Global descriptors informed learning for organic melting-point prediction.RSC advances · 2026Article
- Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- SangsterLogP - the largest publicly available dataset of logP values.Scientific data · 2026Article
- Predicting drug solubility in binary solvent mixtures using graph convolutional networks: a comprehensive deep learning approach.Scientific reports · 2025Article
- Multi-Conformation Enhanced Equivariant Graph Neural Network: Advancing Melting Point Prediction Accuracy for Organic Small Molecules.ACS omega · 2025Article
- Prediction of Melting Points of Chemicals with a Data Augmentation-Based Neural Network Approach.ACS omega · 2025Article
- Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.Chemical research in toxicology · 2025Article
- Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions.ACS omega · 2024Article
- Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds.ACS omega · 2021Article
- Modeling and insights into molecular basis of low molecular weight respiratory sensitizers.Molecular diversity · 2021Article
- Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds.RSC advances · 2020Article
- Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks.Molecules (Basel, Switzerland) · 2019Article
- Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.Journal of chemical information and modeling · 2019Article
- Computational prediction of drug solubility in water-based systems: Qualitative and quantitative approaches used in the current drug discovery and development setting.International journal of pharmaceutics · 2018Review
- Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective.ChemMedChem · 2018Article
- OPERA models for predicting physicochemical properties and environmental fate endpoints.Journal of cheminformatics · 2018Article
- Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.Chemosphere · 2018Article
- In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.Journal of chemical information and modeling · 2017Article
- BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.Molecular informatics · 2016Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMelting 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.