Browsing University of Tartu, Institute of Chemistry, Molecular Technology (Estonia) by Predict from structure

Showing items 1-12 of 12

Aruoja, V.; Moosus, M.; Kahru, A.; Sihtmäe, M.; Maran, U. Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic chemicals for the alga Pseudokirchneriella subcapitata. Chemosphere 2014, 96, 23-32

Published: Villem Aruoja, Maikki Moosus, Anne Kahru, Mariliis Sihtmäe, Uko Maran (2013-06-27)

Abstract: In this paper a set of homogenous experimental algal toxicity data was measured for 50 non-polar narcotic chemicals using the alga Pseudokirchneriella subcapitata in a closed test with a growth rate endpoint. Most of the tested compounds are high volume industrial chemicals that so far lacked published REACH-compliant algal growth inhibition values. The test ...

EcoTox Regression PredictFromStructure
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
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
Könemann, H. Quantitative structure-activity relationships in fish toxicity studies Part 1: Relationship for 50 industrial pollutants. Toxicology 1981, 19, 3, 209–221.

Published: Uko Maran (2013-09-13)

Abstract: LC50-experiments have been conducted using guppies subjected to 72 industrial pollutants. The correlation of the LC50 with several expressions of the hydrophobicity of these chemicals has been studied. Calculated logP(oct) values appeared to satisfy more than HPLC retention indices, solubility data or molecular connectivity indices. One QSAR, with logP(oct) ...

EcoTox Regression PredictFromStructure
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 PredictFromStructure
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 PredictFromStructure
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 PredictFromStructure
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 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
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 PredictFromStructure
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 PredictFromStructure
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 PredictFromStructure

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