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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
refutedsupported
the weight of evidence
8 sources for · 0 against

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.

Evidence for · 8
Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point
2003 · cited by 132
Reviews quantitative structure-property relationships (QSPRs) used to calculate melting points and other properties.
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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.

More for · 7
Advancing material property prediction: using physics-informed machine learning models for viscosity
2024 · cited by 76
Integrates molecular dynamics descriptors and machine learning to accurately predict material properties including melting point.
Machine estimation of drug melting properties and influence on solubility prediction.
2020 · cited by 25
Evaluates machine learning methods and QSPRs to estimate melting properties of various compounds.
Multilayered Variable Selection in QSPR
2017 · cited by 11
Develops predictive QSPR models using structural characteristics to determine melting points of ionic liquids.
Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties
2026 · cited by 3
Applies topological indices and machine learning models to successfully forecast melting points of pharmaceutical compounds.
P2MAT: A machine learning (ML) driven software for Property Prediction of MATerial.
2026 · cited by 1
Demonstrates a data-driven machine learning approach utilizing molecular descriptors to predict the melting points of organic compounds.
Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.
2026 · cited by 0
Uses machine learning to predict phase transition temperatures, including melting points, based on molecular structure.
Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.
2026 · cited by 0
Validates generative modeling techniques using physical property datasets that include melting points.
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first checked05 Aug 2026
judged → SUPPORTED · 8805 Aug 2026
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