Toxicological Evaluation of Ionic Liquids: QSAR Approach for Acetylcholinesterase Enzyme Inhibition.

Ali Ebrahimpoor Gorji, Petri Uusi-Kyyny, Ville Alopaeus

Abstract read

In one paragraph

Article in Chemical research in toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ali Ebrahimpoor GorjiSchool of Chemical Technology, Department of Chemical and Metallurgical Engineering, Research Group of Chemical Engineering, Aalto University, P.O. Box 16100, FI-00076 Aalto, Finland.ORCID 0009-0007-0623-8756

Petri Uusi-KyynySchool of Chemical Technology, Department of Chemical and Metallurgical Engineering, Research Group of Chemical Engineering, Aalto University, P.O. Box 16100, FI-00076 Aalto, Finland.ORCID 0000-0002-8339-4601

Ville AlopaeusSchool of Chemical Technology, Department of Chemical and Metallurgical Engineering, Research Group of Chemical Engineering, Aalto University, P.O. Box 16100, FI-00076 Aalto, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A "quantitative structure-activity relationship" (QSAR) model is developed to predict the toxicity of ionic liquids (ILs) based on the effect on the acetylcholinesterase (AChE) enzyme. A data set of 243 ILs was compiled and randomly divided into training (183 ILs) and test (60 ILs) sets to enable both internal and external validations. To optimize the model performance, a breaking point analysis was performed to identify the most relevant molecular descriptors. The analysis revealed that a set of 11 COSMO-RS quantum chemical descriptors provided near-optimal predictive power, with additional descriptors offering minimal improvement. A multiple linear regression (MLR) model was developed by using these descriptors, incorporating both cationic and anionic molecular features. Internal validation using Leave-One-Out and Leave-Many-Out cross-validation (

Indexed as

AcetylcholinesteraseCholinesterase InhibitorsIonic LiquidsQuantitative Structure-Activity RelationshipAnimalsAcetylcholinesteraseCholinesterase InhibitorsIonic Liquids

Identifiers

PMID41699781

DOI10.1021/acs.chemrestox.5c00475

PMCPMC12997245

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.