Leveraging ChemBERTa and machine learning for accurate toxicity prediction of ionic liquids

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dc.authorid0000-0002-2150-4756
dc.authorid0000-0001-7357-7490
dc.contributor.authorSadaghiyanfam, Safa
dc.contributor.authorKamberaj, Hiqmet
dc.contributor.authorIsler, Yalcin
dc.date.accessioned2026-01-24T12:31:16Z
dc.date.available2026-01-24T12:31:16Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractBackground: Accurately predicting the toxicity of ionic liquids is essential for promoting sustainable chemical applications while mitigating environmental and health risks. The increasing complexity and volume of data inherent in toxicology have stimulated interest in machine learning models because they are attractive approaches that can identify patterns among predictors and responses that may not be obvious through classical statistical methodologies. Methods: This study introduces a hybrid framework that combines ChemBERTa-based chemical structure embeddings with Convolutional Neural Networks (CNNs), XGBoost, and Support Vector Regression (SVR). ChemBERTa embeddings, derived from SMILES strings, were enriched with molecular descriptors and fingerprints, with dimensionality reduced using Principal Component Analysis (PCA). To further enhance performance, model optimization was conducted through Optuna, ensuring the best configuration of hyperparameters. Significant Findings: CNNs demonstrated superior performance, achieving an R-squared value of 0.865, a Root Mean Squared Error (RMSE) of 0.390, and a Pearson correlation coefficient of 0.937. XGBoost followed closely with an R-squared value of 0.824, an RMSE of 0.462, and a Pearson correlation of 0.923. SVR also performed competitively, with an R-squared value of 0.797 and an RMSE of 0.496. Notably, the inclusion of ChemBERTa embeddings significantly enhanced model accuracy, as evidenced by the results of ablation studies. This study highlights the potential of hybrid frameworks that combine deep learning with classical machine learning approaches to predict ionic liquid (IL) toxicity. These findings offer valuable insights for safer chemical design, promoting sustainable innovation while supporting regulatory decision-making.
dc.description.sponsorshipIzmir Katip Celebi University Scientific Research Council Agency [2024-TDR-FEBE-0024]
dc.description.sponsorshipAcknowledgments This study was also supported by Izmir Katip Celebi University Scientific Research Council Agency as project number 2024-TDR-FEBE-0024 for Safa Sadaghiyanfam's doctoral thesis studies. All authors wrote sections of the manuscript and contributed to the manuscript revision, read, and approved the submitted version.
dc.identifier.doi10.1016/j.jtice.2025.106030
dc.identifier.issn1876-1070
dc.identifier.issn1876-1089
dc.identifier.scopus2-s2.0-85218643423
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jtice.2025.106030
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5755
dc.identifier.volume171
dc.identifier.wosWOS:001434685400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofJournal of The Taiwan Institute of Chemical Engineers
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectIonic liquids (ILs)
dc.subjectMachine learning
dc.subjectToxicity prediction
dc.subjectChemBERTa
dc.subjectSupport Vector Regression (SVR)
dc.subjectXGBoost
dc.subjectChemical structure embeddings
dc.subjectTransformer-based models
dc.titleLeveraging ChemBERTa and machine learning for accurate toxicity prediction of ionic liquids
dc.typeArticle

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