QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action.
Anatoly G. Artemenko,Eugene N. Muratov,Victor E. Kuz’min,N. N. Muratov,Ekaterina V. Varlamova,A V Kuz'mina,Leonid Gorb,A Golius,Frances C. Hill,Jerzy Leszczynski,Alexander Tropsha +10 more
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The Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds tested for their toxicity and it was shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation.Abstract:
The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).read more
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Journal ArticleDOI
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
TL;DR: The need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation is emphasized and it is demonstrated that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds.
Book ChapterDOI
Tetrahymena in the laboratory: strain resources, methods for culture, maintenance, and storage.
TL;DR: This chapter presents a brief description of many available Tetrahymena strains and lists possible resources for obtaining viable cultures of T. thermophila and other TetrahYmena species.
Journal ArticleDOI
Alarms about structural alerts
Vinicius M. Alves,Vinicius M. Alves,Eugene N. Muratov,Eugene N. Muratov,Stephen J. Capuzzi,Regina Politi,Yen Low,Rodolpho C. Braga,Alexey V. Zakharov,Alexander Sedykh,Elena Mokshyna,Sherif Farag,Carolina Horta Andrade,Victor E. Kuz’min,Denis Fourches,Alexander Tropsha +15 more
TL;DR: It is demonstrated that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity.
Journal ArticleDOI
From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles
Natalia Sizochenko,Natalia Sizochenko,Bakhtiyor Rasulev,Agnieszka Gajewicz,Victor E. Kuz’min,Victor E. Kuz’min,Tomasz Puzyn,Jerzy Leszczynski +7 more
TL;DR: The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles and suggest different mechanisms of nanotoxicity for these two types of cells.
Journal ArticleDOI
Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.
Vinicius M. Alves,Eugene N. Muratov,Denis Fourches,Judy Strickland,Nicole Kleinstreuer,Carolina Horta Andrade,Alexander Tropsha +6 more
TL;DR: This study aims to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitized individuals, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals.
References
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Journal ArticleDOI
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Journal ArticleDOI
Classification and regression trees
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
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TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins.
TL;DR: The main features of the CoMFA approach, exemplified by analyses of the affinities of 21 varied steroids to corticosteroid and testosterone-binding globulins, and a number of advances in the methodology of molecular graphics are described.
Journal ArticleDOI
Beware of q2
TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
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