Atomistic insights into the morphological dynamics of gold and platinum nanoparticles: MD simulations in vacuum and aqueous media

• Evangelos Voyiatzis,
• Eugenia Valsami-Jones and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 995–1009, doi:10.3762/bjnano.15.81

• signifies the occurrence of a phase transition in a cluster of atoms. Additional atomic parameters are the average potential energy, force, and coordination number per atom. These quantities have also been employed as descriptors in nano-QSAR models to successfully predict the toxicity of NPs [73][74][75

• the acylindricity and asphericity shape parameters. Indirect evidence of NP toxicity and reactivity was obtained by examining surface quantities such as the potential energy of surface atoms, the water–NP surface energy, and some descriptors that are commonly used in nano-QSAR (quantitative structure

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

• Indrasis Dasgupta,
• Totan Das,
• Biplab Das and
• Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

• to the surface of ENMOs to modify their properties and, specifically, the cellular uptake. A lot of computational studies (Table 1) have been reported using nanoscale quantitative structure–activity relationship (nano-QSAR) models (predominantly regression-based) that specifically employ the cellular

• uptake in the PaCa2 cell line [22][23][24][25][26][27]. In the current study, we have performed a distinctive approach by developing nano-QSAR machine learning-based classification models that encompass not only the cellular uptake data of the PaCa2 cell line but also the two additional cell lines HUVEC

A review on the structural characterization of nanomaterials for nano-QSAR models

• Salvador Moncho,
• Eva Serrano-Candelas,
• Jesús Vicente de Julián-Ortiz and
• Rafael Gozalbes

Beilstein J. Nanotechnol. 2024, 15, 854–866, doi:10.3762/bjnano.15.71

• of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess

• component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial’s behavior, preparation, or test conditions) that indirectly reflect its structure. Keywords: descriptors; nanomaterials; nano-QSAR; QSAR; toxicity; Introduction Computational techniques of

• structure of NMs [5][6][7]. The first described nano-QSAR model is from 2009 [8], but the number of relevant nano-QSAR models is growing significantly because new nanoscale descriptors are found [6], and more information on NMs is progressively generated, opening new ways of improving nano-QSARs. This is an

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• metals into organisms. Thus, the present study reports nanoscale quantitative structure–activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2

• cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99–0.89), Q2 values (0.64–0.77), and Q2F1 values (0.99–0.71). Thus, the

• most widely used. Therefore, the joint organismal toxicity should be assessed. Recently, nanoscale quantitative structure–activity relationship (nano-QSAR) models have been successfully applied to investigate the toxicity of NPs. QSAR models for predicting the biological activity of 48 fullerene

Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

• Richard Liam Marchese Robinson,
• Haralambos Sarimveis,
• Philip Doganis,
• Xiaodong Jia,
• Marianna Kotzabasaki,
• Christiana Gousiadou,
• Stacey Lynn Harper and
• Terry Wilkins

Beilstein J. Nanotechnol. 2021, 12, 1297–1325, doi:10.3762/bjnano.12.97

• -fertilisation data were found to be harder to predict, which could reflect different exposure routes. Hence, subsequent analysis focused on the prediction of excess lethality at 120 hours-post fertilisation. The use of two data augmentation approaches, applied for the first time in nano-QSAR research, was

• these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein. Keywords: data augmentation; embryonic zebrafish; machine learning; nanosafety; nano-QSAR

• (i.e., depending on the exposure medium) physicochemical characteristics [18][19][20]. However, concerns have been raised regarding the human health relevance of the endpoints modelled in many nano-QSAR studies [21]. There is a need for models that can predict human safety relevant endpoints

Evaluating the toxicity of TiO₂-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach

• Alicja Mikolajczyk,
• Natalia Sizochenko,
• Ewa Mulkiewicz,
• Anna Malankowska,
• Michal Nischk,
• Przemyslaw Jurczak,
• Seishiro Hirano,
• Grzegorz Nowaczyk,
• Adriana Zaleska-Medynska,
• Jerzy Leszczynski,
• Agnieszka Gajewicz and
• Tomasz Puzyn

Beilstein J. Nanotechnol. 2017, 8, 2171–2180, doi:10.3762/bjnano.8.216

• (Brunauer–Emmett–Teller surface, BET) of nanoparticles were discussed. Keywords: Au/Pd–TiO2 photocatalyst; bimetallic nanoparticles; nanotoxicity; nano-QSAR; second-generation nanoparticles; Introduction Unmodified titania (TiO2) nanoparticles (so-called first-generation NPs) represent a material that

• the nano-QSAR model. These findings clearly demonstrate that the cytotoxicity depends on particle size and surface area, which is in line with the recent experimental results. For instance, Coradeghini et al. [50] investigated the particle size dependent cytotoxicity of Au NPs (0.8–15 nm) to four

• the actual and fitted values with the nano-QSAR model are summarized in Table 3. A plot of experimentally determined vs predicted values for the general model is presented in Figure 3. This plot revealed a good agreement between the observed and predicted values of cytotoxicity for the 17 TiO2-based

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

• Richard L. Marchese Robinson,
• Mark T. D. Cronin,
• Andrea-Nicole Richarz and
• Robert Rallo

Beilstein J. Nanotechnol. 2015, 6, 1978–1999, doi:10.3762/bjnano.6.202

• vitro assays [4][84]. A number of nano-QSAR models have been developed for cytotoxicity [13][85][86][87][88][89][90][91] and some models have also been developed for nanomaterial genotoxicity [9][92][93]. The genotoxicity Assay file template (“a_InvID_genotoxicity_Method.xls”) was designed to capture