Few-Shot Prediction of Toxicity of Ionic Liquids Supported by Attentive Model-Agnostic Meta-Learning.

Karol Baran, Tomasz Derron, Joachim Eichenlaub, Adam Kloskowski

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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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5 · Who and what money

Authors and funding

4 authors.

Karol BaranDepartment of Physical Chemistry, Faculty of Chemistry, Gdansk University of Technology, Narutowicza Str. 11/12, 80-233 Gdansk, Poland.ORCID 0000-0002-5883-4660

Tomasz DerronDepartment of Physical Chemistry, Faculty of Chemistry, Gdansk University of Technology, Narutowicza Str. 11/12, 80-233 Gdansk, Poland.

Joachim EichenlaubDepartment of Dental Prosthetics, Medical University of Gdansk, Sklodowskiej-Curie St. 3c, 80-210 Gdansk, Poland.

Adam KloskowskiDepartment of Physical Chemistry, Faculty of Chemistry, Gdansk University of Technology, Narutowicza Str. 11/12, 80-233 Gdansk, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediction of chemical compounds' toxicity enables efficient and rapid screening at the cost of utilizing experimental data as a foundation for artificial intelligence (AI) models. Given the constraints of limited data availability, few-shot learning techniques, like those based on meta-learning models, may be beneficial. In this study, the ionic liquid (IL) toxicity dataset was employed. Initially, models underwent pretraining on data from related domains before being adapted in a few-shot manner, adhering to the Model-Agnostic Meta-Learning (MAML) algorithm. The MAML algorithm utilized a neural network to predict toxicity based on the descriptors of the molecular structure. MAML enhanced the accuracy of IL toxicity prediction even with limited data, particularly when similar tasks were provided during meta-training, such as between

Indexed as

Ionic LiquidsMachine LearningAlgorithmsAliivibrio fischeriEscherichia coliNeural Networks, ComputerPrediction AlgorithmsIonic Liquids

Identifiers

PMID42135212

DOI10.1021/acs.chemrestox.6c00022

PMCPMC13299051

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LicenceCC BY

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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.