Browsing Partnership for the Assessment of Risks from Chemicals by Submit Date

Showing items 1-5 of 5

Kotli, M.; Piir, G.; Maran, U. Pesticide effect on earthworm lethality via interpretable machine learning. J. Hazard Mater. 2023, 461, 132577

Published: Mihkel Kotli, Geven Piir, Uko Maran (2023-08-18)

Abstract: Earthworms are among the most important animals (invertebrates) for soil health. Many chemical substances released into nature for agricultural development, such as pesticides, may have unwanted effects on those organisms. However, it is essential to understand the extent of the impact of chemicals on soil health first and then make the proper ...

EcoTox Classification PredictFromStructure
Piir, G.; Sild, S.; Maran, U. Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders. Chemosphere 2024, 347, 140671.

Published: Geven Piir, Sulev Sild, Uko Maran (2023-09-14)

Abstract: An abnormal hormonal activity or exposure to endocrine-disrupting chemicals (EDCs) can cause endocrine system malfunction. Among the many interactions EDCs can affect is the disruption of estrogen signalling, which can lead to adverse health effects such as cancer, osteoporosis, neurodegenerative diseases, cardiovascular disease, insulin resistance, and obesity. ...

HumanHealth Classification PredictFromStructure
Kotli, M.; Piir, G.; Maran, U. Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data. ACS Omega 2025, 10, 4732–4744.

Published: Mihkel Kotli, Geven Piir, Uko Maran (2024-10-09)

Abstract: The earthworm is a key indicator species in soil ecosystems. This makes the reproductive toxicity of chemical compounds to earthworms a desired property of determination and makes computational models necessary for descriptive and predictive purposes. Thus, the aim was to develop an advanced Quantitative Structure–Activity Relationship modeling approach for ...

EcoTox Classification PredictFromStructure
Belfield, S. J.; Cronin, M. T. D.; Enoch, S. J.; Firman, J. W. Guidance for Good Practice in the Application of Machine Learning in Development of Toxicological Quantitative Structure-Activity Relationships (QSARs). PLOS ONE, 2023, 18, e0282924.

Published: Georgios Chrysochoou, Sulev Sild (2024-10-21)

Abstract: Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animalintensive in ...

Regression
Piir, G.; Sild, S.; Spilioti, E.; Nikolopoulou, D.; Katsanou, E.; Langezaal, I.; Maran, U. Classification of Thyroid Peroxidase (TPO) Inhibitors Using Transfer Learning with SMILES Embeddings. Chemical Research in Toxicology 2026.

Published: Geven Piir, Sulev Sild, Eliana Spilioti, Dimitra Nikolopoulou, Effrosyni Katsanou, Ingrid Langezaal, Uko Maran (2026-05-27)

Abstract: Thyroid hormones (THs) regulate many processes in mammals and, therefore, affect every organ in the body. Thyroid peroxidase (TPO) is an essential enzyme for the successful biosynthesis of THs. Although TPO inhibition is a well-documented molecular initiating event (MIE) in thyroid hormone system disruption adverse outcome pathways (AOPs), experimental methods ...

HumanHealth Classification PredictFromStructure

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