QsarDB: QSAR and QSPR model repository

• 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 2023. 

  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. Journal of Hazardous Materials 2023 

  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
• Oja, M.; Sild, S.; Piir, G.; Maran, U. Intrinsic aqueous solubility: mechanistically transparent data-driven modeling of drug substances. Pharmaceutics 2022, 14, 2248. 

  Published: Mare Oja, Sulev Sild, Geven Piir, Uko Maran (2022-10-12)

  Abstract: Intrinsic aqueous solubility is a foundation property for understanding chemical, technological, pharmaceutical, and environmental behavior of drug substances. Despite years of solubility research, molecular structure-based prediction of the intrinsic aqueous solubility of drug substances is still under active investigation. This paper describes the authors’ ...

  PhysChem Regression DescriptorCalculation
• Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. Machine learning Quantitative Structure-Property Relationships as a function of ionic liquid cations for the gas-ionic liquid partition coefficient of hydrocarbons. Int. J. Mol. Sci. 2022, 23, 7534. 

  Published: Karl Marti Toots, Sulev Sild, Jaan Leis, Acree Jr., W. E., Uko Maran (2022-06-22)

  Abstract: Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore new ILs are being developed and adapted as innovative chemical environments for different applications where their properties need to be understood on a molecular level. Computational data driven methods provide means for understanding of properties at molecular ...

  Regression DescriptorCalculation
• Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., William E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021, 343, 117573. 

  Published: Karl Marti Toots, Sulev Sild, Jaan Leis, Acree Jr., William E., Uko Maran (2021-09-20)

  Abstract: Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for ...

  Regression DescriptorCalculation
• Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_Risk). 2020. 

  Published: Michael Lawless (2021-09-02)

  Abstract: MUT_Risk Abstract The Ames bacterial mutagenicity test is an important regulatory screen for potential carcinogenicity. In silico prediction of Ames positivity plays two important but somewhat distinct roles in the discovery and development of biologically active compounds. A positive Ames test does not necessarily derail development of an active ingredient ...

  HumanHealth Classification QMRF

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