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the claim
Molecular structure can be used to predict the melting points of substances
the verdict
SUPPORTED
the evidence backs this
Recorded sources
8 sources for · 0 against
Is it true that molecular structure can be used to predict the melting points of substances?
Yes. Multiple studies demonstrate that quantitative structure-property relationships (QSPR) and machine learning models can effectively use molecular structures and descriptors to predict the melting points of various substances.
Verdict: SUPPORTED · 8 sources for, 0 against · Last checked
The analysis
The retrieved papers consistently demonstrate that molecular structure, through QSPR models, topological indices, and machine learning, can be used to predict melting points. There are no papers contradicting this claim.
Evidence for · 8
Reviews quantitative structure-property relationships (QSPRs) used to calculate melting points and other properties.
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Dearden, J. C. (2003). Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point. doi.org. https://doi.org/10.1897/01-363
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More for · 7
Advancing material property prediction: using physics-informed machine learning models for viscosity
Integrates molecular dynamics descriptors and machine learning to accurately predict material properties including melting point.
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Chew, A. K., Sender, M., Kaplan, Z., Chandrasekaran, A., Elk, J. C., Browning, A., Kwak, H., Halls, M., Afzal, M. A. F., Atif, M., & Afzal, F. (2024). Advancing material property prediction: using physics-informed machine learning models for viscosity. doi.org. https://doi.org/10.1186/s13321-024-00820-5
Evaluates machine learning methods and QSPRs to estimate melting properties of various compounds.
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Wyttenbach, N., Niederquell, A., & Kuentz, M. (2020). Machine estimation of drug melting properties and influence on solubility prediction.. doi.org. https://doi.org/10.1021/acs.molpharmaceut.0c00355
Develops predictive QSPR models using structural characteristics to determine melting points of ionic liquids.
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Das, S., Ojha, P. K., & Roy, K. (2017). Multilayered Variable Selection in QSPR. doi.org. https://doi.org/10.4018/ijqspr.2017010108
Applies topological indices and machine learning models to successfully forecast melting points of pharmaceutical compounds.
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Zhang, G., Noureen, S., Azam, S., Abdalla, M. E. M., Dafaalla, M. E., Aslam, A., & Tola, K. A. (2026). Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties. doi.org. https://doi.org/10.1038/s41598-025-33532-3
Demonstrates a data-driven machine learning approach utilizing molecular descriptors to predict the melting points of organic compounds.
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Kamruzzaman, M., Landera, A., Menon, N., Allendorf, M., & Poorey, K. (2026). P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial.. doi.org. https://doi.org/10.1186/s13321-026-01247-w
Uses machine learning to predict phase transition temperatures, including melting points, based on molecular structure.
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N, M. & H, K. (2026). Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.. doi.org. https://doi.org/10.1002/minf.70044
Validates generative modeling techniques using physical property datasets that include melting points.
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Y, U. & H, K. (2026). Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.. doi.org. https://doi.org/10.1021/acs.jcim.5c03220
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What does the evidence say?
The retrieved papers consistently demonstrate that molecular structure, through QSPR models, topological indices, and machine learning, can be used to predict melting points. There are no papers contradicting this claim.
Receipt History
first checked05 Aug 2026
judged → SUPPORTED · 8805 Aug 2026
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