QsarDB
https://qsardb.org:443/repository
The QsarDB repository stores, indexes, preserves, and distributes QSAR/QSPR models.2024-03-29T06:56:11ZHewitt, 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.
http://hdl.handle.net/10967/260
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 barrier, then it will not be a reproductive toxicant. Such a model could be placed at the start of any integrated testing strategy. To develop these models the literature was reviewed to obtain data relating to the transfer of molecules across the placenta. A reasonable number of data were obtained and are suitable for the modelling of the ability of a molecule to cross the placenta. Clearance or transfer indices data were sought due to their ability to eliminate inter-placental variation by standardising drug clearance to the reference compound antipyrine. Modelling of the permeability data indicates that (Q)SARs with reasonable statistical fit can be developed for the ability of molecules to cross the placental barrier membrane. Analysis of the models indicates that molecular size, hydrophobicity and hydrogen-bonding ability are molecular properties that may govern the ability of a molecule to cross the placental barrier.
2024-03-07T14:20:55ZPiir, G.; Sild, S.; Maran, U. Interpretable machine learning for the identification of estrogen receptor agonists, antagonists, and binders. Chemosphere 2023.
http://hdl.handle.net/10967/259
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. Knowing which chemical can act as an EDC is a significant advantage and a practical necessity. New Approach Methodologies (NAM) computational models offer a quick and cost-effective solution for preliminary hazard assessment of chemicals without animal testing. Therefore, a machine learning approach was used to investigate the relationships between estrogen receptor (ER) activity and chemical structure to identify chemicals that can interact with ER. For this purpose, the consolidated in vitro assay data from ToxCast/Tox21 projects was used for developing Random Forest classification models for ER binding, agonists, and antagonists. The overall classification prediction accuracy reaches up to 82%, depending on whether the model predicted agonists, antagonists, or compounds that bind to the active site. Given the imbalance in endocrine disruption data, the derived models are good candidates for deprioritising chemicals and reducing animal testing. The interpretation of theoretical molecular descriptors of the models was consistent with the molecular interactions known in the ligand binding pocket. The estimated class probabilities enabled the analysis of the applicability domain of the developed models and the assessment of the predictions’ reliability, followed by the guidelines for interpreting prediction results. The models are openly accessible and usable at QsarDB.org according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.
2023-09-14T13:44:47ZKotli, M.; Piir, G.; Maran, U. Pesticide effect on earthworm lethality via interpretable machine learning. Journal of Hazardous Materials 2023
http://hdl.handle.net/10967/258
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 decisions for regulatory or commercial purposes. We hypothesize that there is an expressible quantitative structure-activity relationship (QSAR) between the structure of pesticide compounds and the acute toxicity effect of earthworm species Eisenia fetida. The description of this relationship allows for a better assessment of the impact of chemicals on the said earthworm. To describe this relationship, a dataset of chemicals was collected from open-access sources to develop a mathematical model. A novel approach, combining genetic algorithm and Bayesian optimization, was used to select structural features into the model and to optimize model parameters. The final QSAR classification model was created with the Random Forest algorithm and exhibited good prediction Accuracy of 0.78 on training set and 0.80 on test set. The model representation follows FAIR principles and is available on QsarDB.org.
2023-08-18T08:26:20ZOja, M.; Sild, S.; Piir, G.; Maran, U. Intrinsic aqueous solubility: mechanistically transparent data-driven modeling of drug substances. Pharmaceutics 2022, 14, 2248.
http://hdl.handle.net/10967/257
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’ systematic data-driven modelling in which two fit-for-purpose training data sets for intrinsic aqueous solubility were collected and curated, and three quantitative structure-property relationships were derived to make predictions for the most recent solubility challenge. All three models are performing well individually, while being mechanistically transparent and easy to understand. Molecular descriptors involved in the models are related to the following key steps in the solubility process: dissociation of the molecule from the crystal, formation of a cavity in the solvent, and insertion of the molecule into the solvent. A consensus modeling approach with these models remarkably improved prediction capability and reduced the number of strong outliers by more than two times. The performance and outliers of the second solubility challenge predictions were analyzed retrospectively. All developed models have been published in the QsarDB repository according to FAIR principles and can be used without restrictions for exploring, downloading, and predictions.
2022-10-12T13:54:19Z