Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

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

doi:10.1016/j.chemosphere.2018.11.014

Cited by 65 publications

(44 citation statements)

References 41 publications

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“…Experimental in vitro characteristics and exposure conditions are important variables in the representation of a potential toxicity since the same type of NPs may exhibit diverse effects in different biological conditions. 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].…”

Section: Molecular Structures' Codificationmentioning

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: Molecular Structures' Codificationmentioning

confidence: 99%

Section: Molecular Structures' Codificationmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is to be noted, however, in some cases, the molecular structure is not informative to build up a predictive model of endpoints [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]. Meanwhile, the definition of a model as a mathematical function of experimental conditions (after consultations with experimentalists) is a shorter and consequently more attractive way to solve the corresponding task.…”

Section: The Third Weirdness Of Qspr/qsarmentioning

confidence: 99%

QSPR/QSAR: State-of-Art, Weirdness, the Future

Toropov

Toropova

2020

Molecules

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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“…The quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. Model quality was evaluated using adjusted determination coefficient and was shown to be satisfactory [43].…”

Section: Metal Oxidesmentioning

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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Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

Cited by 65 publications

References 41 publications

Practices and Trends of Machine Learning Application in Nanotoxicology

Practices and Trends of Machine Learning Application in Nanotoxicology

QSPR/QSAR: State-of-Art, Weirdness, the Future

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

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