Predictive Nanotoxicology

Khan, Bilal; Cohen, Yoram

doi:10.1002/9781119817512.ch9

Cited by 3 publications

(3 citation statements)

References 154 publications

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“…It has been proven that ML can be used to identify nanomaterial properties and ex posure conditions that influence cellular and organism toxicity, thus providing infor mation required for risk assessment and safe-by-design approaches in the developmen of new nanomaterials [96]. Huang et al [97] combined ML with high-throughput in vitro bioassays to develop a model to predict the toxicity of metal oxide nanoparticles to im for cell viability for green AgNPs.…”

Section: Nanomaterials Toxicitymentioning

confidence: 99%

Application of Machine Learning in Material Synthesis and Property Prediction

Huang,

Guo,

Chen

et al. 2023

Materials

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Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.

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Section: Nanomaterials Toxicitymentioning

confidence: 99%

Application of Machine Learning in Material Synthesis and Property Prediction

Huang,

Guo,

Chen

et al. 2023

Materials

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“…13 A diverse range of ML attempts have been made in the context of nanotoxicology, contributing to our understanding of the relationships between NM properties and their potential effects on living systems. 14,15 ML models can utilize physicochemical and structural properties, such as particle size, surface charge, and composition, to estimate the likelihood of adverse effects. 9 Additionally, ML techniques have been exploited for establishing QSAR, shedding light on the molecular mechanisms underlying toxicity and guiding safer-by-design NM.…”

mentioning

confidence: 99%

“…ML enables toxicity prediction by training models on data sets of NMs with known outcomes, making them well-suited for the multifaceted nature of NM toxicity . A diverse range of ML attempts have been made in the context of nanotoxicology, contributing to our understanding of the relationships between NM properties and their potential effects on living systems. , …”

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confidence: 99%

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

Scott-Fordsmand,

Gomes,

Pokhrel

et al. 2024

ACS Appl. Mater. Interfaces

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The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1−10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model Enchytraeus crypticus (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10%Fe-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe 3 O 4 NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to E. crypticus (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration−response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration−response modeling analysis.

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Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

Gou,

Zafar,

Fatima

et al. 2024

Front. Biosci. (Landmark Ed)

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Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.

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Predictive Nanotoxicology

Cited by 3 publications

References 154 publications

Application of Machine Learning in Material Synthesis and Property Prediction

Application of Machine Learning in Material Synthesis and Property Prediction

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

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