QsarDB: QSAR and QSPR model repository

• Käärik, M.; Krjukova, N.; Maran, U.; Oja, M.; Piir, G.; Leis, J. Nanomaterial texture-based machine learning of ciprofloxacin adsorption on nanoporous carbon. Int. J. Mol. Sci. 2024, 25, 11696. 

  Published: Maike Käärik, Nadežda Krjukova, Uko Maran, Mara Oja, Geven Piir, Jaan Leis (2024-10-25)

  Abstract: Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular ...

  Regression
• 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
• 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 DescriptorCalculation
• Toots, K. M.; Sild, S.; Leis, J.; Acree, W. E.; Maran, U. Exploring the influence of ionic liquid anion structure on gas-ionic liquid partition coefficients of organic solutes using machine learning. Langmuir, 2024, 40, 23714–23728 

  Published: Karl Marti Toots, Sulev Sild, Jaan Leis, William E. Acree, Uko Maran (2024-09-02)

  Abstract: This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was ...

  Regression
• Zukić, S.; Osmanović, A.; Harej, A.; Kraljević Pavelić, S.; Špirtović-Halilović, S.; Veljović, E.; Roca, S.; Trifunović, S.; Završnik, D.; Maran, U. Data driven modelling of substituted pyrimidine and uracil-based derivatives validated with newly synthesized and antiproliferative evaluated compounds. Int. J. Mol. Sci. 2024, 25, 9390 

  Published: Selma Zukic, Uko Maran (2024-08-21)

  Abstract: The pyrimidine heterocycle plays an important role in anticancer research. In particular, the py-rimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning QSAR models that allow for generalization and predictive capabilities in the search for new active ...

  Regression
• Hewitt, M.; Madden, J.C.; Rowe, P.H.; Cronin, M.T.D. Structure-based modelling in reproductive toxicology: (Q)SARs for the placental barrier. SAR QSAR Environ. Res. 2007, 18, 57-76. 

  Published: Sam Belfield (2024-03-07)

  Abstract: The replacement of animal testing for endpoints such as reproductive toxicity is a long-term goal. This study describes the possibilities of using simple (quantitative) structure-activity relationships ((Q)SARs) to predict whether a molecule may cross the placental membrane. The concept is straightforward, if a molecule is not able to cross the placental ...

  Toxicokinetics Regression
• 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 DescriptorCalculation
• 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 DescriptorCalculation

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