AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach
Year: 2025
Type: article
Abstract: In this paper, we propose a robust deep-learning model based on a Quantitative Structure - Property Relationship (QSPR) approach for estimating the critical temperature (TC), critical pressure (PC), acentric factor (ACEN) and normal boiling point (NBP) of any C, H, O, N, S, P, F, Cl, Br, I molecule. The Mordred calculator was used to determine 247 descriptors to characterize the molecules considered in this work. For each evaluated property, multiple neural networks were trained within a bagging framework. The predictions from the final ensemble were successfully tested against a large set of experimental data comprising more than 1700 molecules and compared with those from different recent learning models found in the literature. Comprehensive comparisons and extensive testing highlight the robustness and predictive power of the newly proposed multimodal learning model. The developed prediction tool is available on a website at https://lrgp-thermoppt.streamlit.app/ . Furthermore, a source code for implementing the trained models in Python is available via github https://github.com/bounac80/AI-ThermPpt .
Source: Journal of Cheminformatics
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
OpenAlex ID: W4413807462