In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach

Zhang, Hui; Chen, Qingyi; Xiang, Mingli; Ma, C; Huang, Qi; Yang, Shengyong

doi:10.1016/j.tiv.2008.09.017

Cited by 65 publications

(40 citation statements)

References 29 publications

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“…Finally 246 tested drugs and drug like molecules from the publication by Zhang et al . were added to the collected data . The labels “active” and “inactive” were adapted from the publication.…”

Section: Methodsmentioning

confidence: 99%

“…A disadvantage, however, is the lack of suitable, large datasets which might serve as basis for predictive machine learning models. The two biggest published data sets for mitochondrial toxicity are the Zhang data set (246 compounds) and the Tox21 dataset (5403 compounds). The Tox21 dataset originates from the Tox21 challenge where the mitochondrial membrane potential was assessed in a high throughput screening assay .…”

Section: Introductionmentioning

confidence: 99%

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Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Hemmerich

Troger

Füzi

et al. 2020

Molecular Informatics

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Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Hemmerich

Troger

Füzi

et al. 2020

Molecular Informatics

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show abstract

“…[13][14][15][19][20][21] However, very few profilers have dealt with toxicity induced by mitochondrial dysfunction. 1,22,23 This is, in part, due to the number of mechanisms by which a chemical could induce mitochondrial dysfunction 24 . An additional complication is that a single chemical might have the ability to induce more than one of these mechanisms, making it difficult to define a single MIE within the AOP paradigm.…”

Section: -12mentioning

confidence: 99%

“…[1][2][3][4][5][6][7] These alternatives have been developed employing in silico, in chemico and in vitro methods focussing on replacing or reducing animals used in short-term toxicity tests 8 . In order to be relevant, and useful, for regulatory assessment these alternatives should be based upon specific in vivo endpoints.…”

Section: Introductionmentioning

confidence: 99%

Development of an in Silico Profiler for Mitochondrial Toxicity

Nelms

Mellor

Cronin

et al. 2015

Chem. Res. Toxicol.

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This study outlines the analysis of mitochondrial toxicity for a variety of pharmaceutical drugs extracted from Zhang et al. These chemicals were grouped into categories based upon structural similarity. Subsequently, mechanistic analysis was undertaken for each category to identify the Molecular Initiating Event driving mitochondrial toxicity. The mechanistic information elucidated during the analysis enabled mechanism-based structural alerts to be developed and combined together to form an in silico profiler. This profiler is envisaged to be used to develop chemical categories based upon similar mechanisms as part of the Adverse Outcome Pathway paradigm. Additionally, the profiler could be utilised in screening large dataset in order to identify chemicals with the potential to induce mitochondrial toxicity.

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“…A three-stage manual descriptor selection process was performed: (1) descriptors with too many zero values or the same values (descriptors of Tween HS) were eliminated; (2) descriptors with very small standard deviation values (<0.5%) were removed; (3) a particular descriptor was chosen to represent a group of highly correlated variables (correlation coefficients >0.80), thereby minimizing the redundancy and overlapping of the descriptors. Since the ranges of descriptor values influence the quality of the models generated, we normalized the rest descriptor values to a range of 0 to 1 [28].…”

Section: Solubility Studiesmentioning

confidence: 99%

Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs

Yang

et al. 2011

Journal of Nanomaterials

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We developed the quantative structure-property relationships (QSPRs) models to correlate the molecular structures of surfactant, cosurfactant, oil, and drug with the solubility of poorly water-soluble 2-aryl propionic acid nonsteroidal anti-inflammatory drugs (2-APA-NSAIDs) in self-emulsifying drug delivery systems (SEDDSs). The compositions were encoded with electronic, geometrical, topological, and quantum chemical descriptors. To obtain reliable predictions, we used multiple linear regression (MLR) and artificial neural network (ANN) methods for model development. The obtained equations were validated using a test set of 42 formulations and showed a great predictive power, and linear models were found to be better than nonlinear ones. The obtained QSPR models would greatly facilitate fast screening for the optimal formulations of SEDDS at the early stage of drug development and minimize experimental effort.

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In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach

Cited by 65 publications

References 29 publications

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity

Development of an in Silico Profiler for Mitochondrial Toxicity

Development of Quantitative Structure-Property Relationship Models for Self-Emulsifying Drug Delivery System of 2-Aryl Propionic Acid NSAIDs

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