ArticleACS omega2025
Prediction of Standard Combustion Enthalpy of Organic Compounds Combining Machine Learning and Chemical Graph Theory: A Strategy.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Predicting fire consequences with the transformer model based on multimodal feature fusion.Journal of cheminformatics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of thermochemical properties such as the standard enthalpy of combustion is essential for the design and evaluation of energetic materials. In this study, the prediction of this thermochemical property is proposed through a QSPR strategy that combines machine learning and chemical graph theory. The data set consisted of 3477 organic compounds. SMILES codes were used for each molecule to construct their molecular graphs, from which topological indices such as Estrada, Wiener, and Gutman, as well as centrality measures, were calculated. These descriptors served as predictors in supervised learning models, with tree-based ensemble models showing the best performance. The best-performing model, random forest, achieved the following metrics on the test set:
Identifiers
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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.