QsarDB: QSAR and QSPR model repository

• 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
• 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
• 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
• 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
• 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
• 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
• Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_NIHS). 2020. 

  Published: Michael Lawless (2021-09-02)

  Abstract: MUT_NIHS 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 (AI), ...

  HumanHealth Classification QMRF
• Daga, P. ADMET Predictor - Bacterial mutagenicity model (MUT_m1535). 2020. 

  Published: Michael Lawless (2021-09-02)

  Abstract: MUT_m1535 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 (AI), ...

  HumanHealth Classification QMRF

View more