• 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
• Garcia-Sosa, A. T.; Maran, U. Combined Naïve Bayesian, chemical fingerprints, and molecular docking classifiers to model and predict androgen receptor binding activity data for environmentally- and health-sensitive substances. Int. J. Mol. Sci. 2021, 22, 6695. 

  Published: Alfonso T. Garcia-Sosa, Uko Maran (2021-06-22)

  Abstract: Many chemicals that enter the environment, food chain, and the human body can disrupt androgen-dependent pathways and mimic hormones and therefore, may be responsible for multiple diseases from reproductive to tumor. Thus, modeling and predicting androgen binding activity is an important area of research. The aim of the current study was to find a method or ...

  HumanHealth Classification
• Zukić, S.; Maran, U. Modelling of antiproliferative activity measured in HeLa cervical cancer cells in a series of xanthene derivatives. SAR QSAR Environ. Res. 2020, 31, 905-921. 

  Published: Selma Zukic, Uko Maran (2020-11-25)

  Abstract: Cancer is continually one of the leading causes of death in humans, and new drug substances are therefore being developed. Thus, the anti-cancer activity of xanthene derivatives has become an important topic in the development of new and potent anti-cancer drug substances. Previously published novel series of xanthen-3-one and xanthen-1,8-dione derivatives ...

  Regression DescriptorCalculation
• Piir, G.; Sild, S.; Maran, U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. Chemosphere 2021, 262, 128313. 

  Published: Geven Piir, Sulev Sild, Uko Maran (2020-07-16)

  Abstract: Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. ...

  HumanHealth Classification DescriptorCalculation
• Käärik, M.; Arulepp, M.; Käärik, M.; Maran, U.; Leis, J. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors. Carbon 2020, 158, 494-504. 

  Published: Maike Käärik, Mati Arulepp, Meelis Käärik, Uko Maran, Jaan Leis (2019-11-06)

  Abstract: The development of nanoporous carbon-based energy storage is a fast-growing area. To assist these developments, it is necessary to establish simple criteria and relationships between electric double-layer (EDL) capacitance and the nature of porous carbon used as an electrode material. Under special attention is carbide-derived carbon (CDC) due to high content ...

  Regression
• Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, 59, 2442-2455. 

  Published: Mare Oja, Sulev Sild, Uko Maran (2019-02-21)

  Abstract: Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide range of pH-s ...

  Toxicokinetics Classification DescriptorCalculation
• Käärik, M.; Maran, U.; Arulepp, M.; Perkson, A.; Leis, J. Quantitative nano-structure-property relationships for the nanoporous carbon: Predicting the performance of energy storage materials. ACS Appl. Energy Mater. 2018, 1, 4016-4024 

  Published: Uko Maran, Maike Käärik, Mati Arulepp, Anti Perkson, Jaan Leis (2018-07-26)

  Abstract: Nanoporous carbon-based energy storage is a fast-growing research field thanks to high energy densities of carbon electrodes with nanoporous amorphous texture. To support the developments on electrical double-layer based ultra-capacitors it is necessary to improve understanding about relationships between the porous structure and energy storage behavior of ...

  Regression
• Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci. 2018, 123, 429-440. 

  Published: Mare Oja, Uko Maran (2018-07-06)

  Abstract: The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane ...

  Toxicokinetics Regression
• Viira, B.; Garcia-Sosa, A. T.; Maran, U. Chemical Structure and Correlation Analysis of HIV-1 NNRT and NRT Inhibitors and Database-Curated, Published Inhibition Constants with Chemical Structure in Diverse Datasets. J. Mol. Graph. Model. 2017, 76, 205-223. 

  Published: Viira, B., Garcia-Sosa, A. T., Maran, U. (2017-06-16)

  Abstract: Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many ...

  Regression
• Oja, M.; Maran, U. Quantitative structure-permeability relationships at various pH values for neutral and amphoteric drugs and drug-like compounds. SAR QSAR Environ. Res. 2016, 27, 813-832. 

  Published: Mare Oja, Uko Maran (2016-09-14)

  Abstract: Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.4 and 9 were measured ...

  Toxicokinetics Regression
• Oja, M.; Maran, U. Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds. SAR QSAR Environ. Res. 2015, 26, 701-719. 

  Published: Mare Oja, Uko Maran (2015-08-17)

  Abstract: Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and obtain good permeability estimates for prediction purposes. This study concentrates on relationships between ...

  Toxicokinetics Regression
• Oja, M.; Maran, U. The permeability of artificial membrane for the wide range of pH in human gastrointestinal tract: experimental measurements and quantitative structure-activity relationship. Mol. Inf. 2015, 34, 493–506. 

  Published: Mare Oja, Uko Maran (2015-02-26)

  Abstract: In silico models for membrane permeability have been based on values measured for single pH. Depending on the diet (fasted/fed state) and part of human intestinal the range of pH varies approximately from 2.4 to 8.0. This motivated to study and model membrane permeability of chemicals considering range of pH in the human intestinal. For this effective membrane ...

  Toxicokinetics Regression
• Martin, D.; Sild, S.; Maran, U.; Karelson, M. QSPR Modeling of the Polarizability of Polyaromatic Hydrocarbons and Fullerenes. J. Phys. Chem. C 2008, 112, 13, 4785–4790. 

  Published: Geven Piir (2014-10-10)

  Abstract: Polarizability is one of the key properties determining the nonlinear optical effects of the new materials. In the current study, a quantitative structure - property relationship approach is used to model the polarizability of polyaromatic hydrocarbons (PAHs) and fullerenes. The model is derived using the data set of 40 PAHs and fullerenes and includes just ...

  Regression
• Martin, D.; Maran, U.; Sild, S.; Karelson, M. QSPR Modeling of Solubility of Polyaromatic Hydrocarbons and Fullerene in 1-Octanol and n-Heptane. J. Phys. Chem. B 2007, 111, 33, 9853-9857. 

  Published: Geven Piir (2014-10-10)

  Abstract: Solubility of polyaromatic hydrocarbons (PAH) and carbon nanostructures is important both from the technical and environmental points of view. In the present work, two general quantitative structure - property relationship (QSPR) models were developed, describing the solubility of PAH-s and fullerene (C60 ) in two different condensed media (1-octanol and ...

  Regression
• Piir, G.; Sild, S.; Maran, U. Classifying bio-concentration factor with random forest algorithm, influence of the bio-accumulative vs. non-bio-accumulative compound ratio to modelling result, and applicability domain for random forest model. SAR QSAR Environ. Res. 2014, 25, 967-981. 

  Published: Geven Piir, Sulev Sild, Uko Maran (2014-07-30)

  Abstract: In environmental risk assessment, the bio-concentration factor (BCF) is a widely used parameter in the estimation of the bio-accumulation potential of chemicals. BCF data often have an uneven distribution of classes (bio-accumulative vs. non-bio-accumulative), which could severely bias the classification results towards the prevailing class. The present study ...

  EnvironFate Classification DescriptorCalculation
• Piir, G.; Sild, S.; Roncaglioni, A.; Benfenati, E.; Maran, U. QSAR model for the prediction of bio-concentration factor using aqueous solubility and descriptors considering various electronic effects. SAR QSAR Environ. Res. 2010, 21, 7-8, 711-729. 

  Published: Geven Piir (2014-07-01)

  Abstract: The in silico modelling of bio-concentration factor (BCF) is of considerable interest in environmental sciences, because it is an accepted indicator for the accumulation potential of chemicals in organisms. Numerous QSAR models have been developed for the BCF, and the majority utilize the octanol/water partition coefficient (logP) to account for the penetration ...

  EnvironFate Regression

View more