Descriptors in Nano-QSAR/QSPR Modeling

Wyrzykowska, Ewelina; Jagiełło, Karolina; Rasulev, Bakhtiyor; Puzyn, Tomasz

doi:10.1201/9780429341373-6

Cited by 7 publications

(7 citation statements)

References 1 publication

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“…Different descriptors used in nano‐QSAR modeling with their advantages, disadvantages, software, and online resources can be found in published literature. [ 79 ] The descriptors might be classified into experimental and theoretical descriptors. In the field of nano‐QSAR, experimental descriptors are terms used to describe the conditions or variables of an experiment related to the study of the relationship between the structures and activities of NMs.…”

Section: Nanodescriptors For Cellular Toxicitymentioning

confidence: 99%

Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Singh,

Varma,

Rai

et al. 2024

Advanced Intelligent Systems

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The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors underscores the need for advanced risk assessment methods to safeguard human health and the environment. Traditional labor‐intensive approaches have given way to computational methods. This review integrates recent developments in chemical and nano‐quantitative structure‐activity relationship (QSAR) with machine learning and computational modeling, offering a comprehensive predictive assessment of NMs and chemicals. It explores nanodescriptors, their role in predicting toxicity, and the amalgamation of machine learning algorithms with chemical and nano‐QSAR for improved risk assessment accuracy. The article also investigates computational modeling techniques like molecular dynamics simulations, molecular docking, and molecular mechanics/quantum mechanics for predicting physical and chemical properties. By consolidating these approaches, the review advocates for a more accurate and efficient means of assessing risks associated with NMs/chemicals, promoting their safe utilization and minimizing adverse effects on human health and the environment. A valuable resource for researchers and practitioners, informed decision‐making, advancing our understanding of potential risks, is facilitated. Beyond studying systems at various scales, computational modeling integrates data from diverse sources, enhancing risk assessment accuracy and fostering the safe use of NMs/chemicals while minimizing their impact on human health and the environment.

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Section: Nanodescriptors For Cellular Toxicitymentioning

confidence: 99%

Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Singh,

Varma,

Rai

et al. 2024

Advanced Intelligent Systems

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“…In the study of the crystallization tendency of metal−organic nanocapsules, when using multidimensional data sets for training, eXtreme Gradient Boosting (XGBoost) had the highest prediction accuracy among 9 training algorithms, reaching 90%. 118 The construction process of machine learning models for nanoparticle risk assessment is shown in Figure 5.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges

Zeng,

Lv,

Sun

et al. 2024

Environ. Health

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Understanding the bioaccumulation of nanomaterials (NMs) by organisms is essential in evaluating their potential ecotoxicity. However, the experimental determination of bioaccumulation is substantially challenging, which spawned the development of prediction approaches via establishing models for various NMs. Conventional modeling approaches, such as the biotic ligand model (BLM), partition coefficients, accumulation factor models, and quantitative structure−activity relationship (QSAR) models, were initially used in the application of NMs, aiming to provide a reliable quantitative dose in a resource-saving way. These methods, which are based on the uptake patterns of substances, probably lead to deviated results due to the different uptake behaviors of NMs. In this study, currently developed models to evaluate the bioaccumulation of NMs are critically reviewed, with their feasibilities and limitations being analyzed. In addition, the recently developed machine learning amended models have taken great efforts in realizing biological behaviors of NMs in organisms by providing in silico predictions. Though this data-driven approach has limitations in mechanism exploration, it may give different insights into the bioaccumulation model establishment and critical feature identification.

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“…Molecular descriptors, mathematical entities that encode relevant structural and physicochemical properties of nanomaterials, can be generated by theoretical methods or by standardized experiments. They are one of the most important elements in computational modelling studies in medicinal/environmental chemistry, toxicology, pharmacology, genomics and drug design [98][99][100]. Computed theoretical descriptors that capture important structural properties of nanomaterials provide diverse sources of chemical properties and a broad coverage of the vast chemical property space describing all possible nanomaterials.…”

Section: Descriptorsmentioning

confidence: 99%

Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology

Karakus¹,

Winkler²

2021

Nano Futures

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The rapid rise of nanotechnology has resulted in a parallel rise in the number of products containing nanomaterials. The unusual properties that nano forms of materials exhibit relative to the bulk has driven intense research interest and relatively rapid adoption by industry. Regulatory agencies are charged with protecting workers, the public, and the environment from any adverse effects of nanomaterials that may also arise because of these novel physical and chemical properties. They need data and models that allow them to flag nanomaterials that may be of concern, while balancing potential stifling of commercial innovation. Roadmaps for the future of safe nanotechnology were defined more than a decade ago, but many roadblocks identified in these studies remain. Here, we discuss the roadblocks that are still hindering the effective application of informatics and predictive computational nanotoxicology methods from providing more effective guidance to nanomaterials regulatory agencies and safe-by-design rationale for industry. We describe how developments in high throughput synthesis, characterization, and biological assessment of nanomaterials will overcome many of these roadblocks, allowing a clearly defined roadmap for computational design of effective but safe-by-design nanomaterials to be realized.

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Descriptors in Nano-QSAR/QSPR Modeling

Cited by 7 publications

References 1 publication

Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Predicting Bioaccumulation of Nanomaterials: Modeling Approaches with Challenges

Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology

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