Quasi-SMILES-Based Nano-Quantitative Structure–Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

Trinh, Tung X.; Choi, Jang Sik; Jeon, Hyunpyo; Byun, Hyung‐Gi; Yoon, Tae-Hyun; Kim, Jongwoon

doi:10.1021/acs.chemrestox.7b00303

Cited by 84 publications

(46 citation statements)

References 57 publications

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“…Six studies, out of the 86 gathered, used Genetic Algorithm (GA) for feature selection [30][31][32][33][34][35]. Five of them used Pearson correlation coefficients between pairs of variables to identify those that correlate with the endpoint or correlations among variables to avoid inter-correlations [36][37][38][39]. Few of the studies applied more than one feature selection technique.…”

Section: Feature Selectionmentioning

confidence: 99%

“…This makes the development of classic QSAR difficult [82]. Toropova et al [83] suggested a quasi-SMILES approach to represent molecular structures, p-chem properties, and experimental conditions (eclectic data) with NMs [37,82]. The eclectic data are translated into optimal nano-descriptors (the sum of weights of quasi-SMILES) for the outcome prediction and Monte Carlo optimization is used to select the optimal descriptors.…”

Section: Molecular Structures' Codificationmentioning

confidence: 99%

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Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Molecular Structures' Codificationmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

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“…Statistical parameters of the models were satisfactory [78]. Nano-QSAR models were constructed to predict the toxicity of 20 MWCNTs types (276 data records) towards human lung cells by using a quasi-SMILES optimal descriptor [80]. Quasi-SMILES were used to represent the physico-chemical properties and experimental conditions for the MWCNTs: Diameter, length, surface area, in vitro toxicity assay, cell line, exposure time, and dose.…”

Section: Multi-walled Carbon Nanotubes (Mwcnts)mentioning

confidence: 99%

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

2019

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Although nanotechnology is a new and rapidly growing area of science, the impact of nanomaterials on living organisms is unknown in many aspects. In this regard, it is extremely important to perform toxicological tests, but complete characterization of all varying preparations is extremely laborious. The computational technique called quantitative structure-activity relationship, or QSAR, allows reducing the cost of time-and resource-consuming nanotoxicity tests. In this review, (Q)SAR cytotoxicity studies of the past decade are systematically considered. We regard here five classes of engineered nanomaterials (ENMs): Metal oxides, metal-containing nanoparticles, multi-walled carbon nanotubes, fullerenes, and silica nanoparticles. Some studies reveal that QSAR models are better than classification SAR models, while other reports conclude that SAR is more precise than QSAR. The quasi-QSAR method appears to be the most promising tool, as it allows accurately taking experimental conditions into account. However, experimental artifacts are a major concern in this case.

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“…Since its discovery, QSAR model has been widely applied to drug design as well as the prediction of their toxicity . Later, many studies demonstrated that the general QSAR paradigm could be further extended to nanomaterials and develop “nano‐QSAR” model which can establish predictive relationship between their physicochemical properties and biological effects (both desired and undesired) . This success largely depends on the application of high‐throughput techniques coupled with cluster analysis and data mining, by which a large number of biological data could be achieved and then integrated into the large‐scale modeling.…”

Section: Introductionmentioning

confidence: 99%

A Safe‐by‐Design Strategy towards Safer Nanomaterials in Nanomedicines

et al. 2019

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The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.201805391.The marriage of nanotechnology and medicine offers new opportunities to fight against human diseases. Benefiting from their unique optical, thermal, magnetic, or redox properties, a wide range of nanomaterials have shown potential in applications such as diagnosis, drug delivery, or tissue repair and regeneration. Despite the considerable success achieved over the past decades, the newly emerging nanomedicines still suffer from an incomplete understanding of their safety risks, and of the relationships between their physicochemical characteristics and safety profiles. Herein, the most important categories of nanomaterials with clinical potential and their toxicological mechanisms are summarized, and then, based on this available information, an overview of the principles in developing safe-by-design nanomaterials for medical applications and of the recent progress in this field is provided. These principles may serve as a starting point to guide the development of more effective safe-by-design strategies and to help identify the major knowledge and skill gaps.

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Quasi-SMILES-Based Nano-Quantitative Structure–Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells

Cited by 84 publications

References 57 publications

Practices and Trends of Machine Learning Application in Nanotoxicology

Practices and Trends of Machine Learning Application in Nanotoxicology

Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials

A Safe‐by‐Design Strategy towards Safer Nanomaterials in Nanomedicines

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