{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang Y"],"funding":["Hong Kong PhD Fellowship Scheme","Hong Kong Research Grants Council Early Career Scheme","Hong Kong University of Science and Technology"],"pagination":["4694"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11477705"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["29(19)"],"pubmed_abstract":["The propagation rate coefficient (<i>k</i><sub>p</sub>) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The <i>k</i><sub>p</sub> in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict <i>k</i><sub>p</sub> more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monome"],"journal":["Molecules (Basel, Switzerland)"],"pubmed_title":["A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization."],"pmcid":["PMC11477705"],"funding_grant_id":["26214522","Start-Up Fund","PF2278203"],"pubmed_authors":["Fang Y","Gao H","Wang Y","Zhou H"],"additional_accession":[]},"is_claimable":false,"name":"A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.","description":"The propagation rate coefficient (<i>k</i><sub>p</sub>) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The <i>k</i><sub>p</sub> in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict <i>k</i><sub>p</sub> more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monome","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2025-04-04T13:54:41.846Z","creation":"2025-04-04T13:54:41.846Z"},"accession":"S-EPMC11477705","cross_references":{"pubmed":["39407622"],"doi":["10.3390/molecules29194694"]}}