The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

• Dimitra-Danai Varsou,
• Arkaprava Banerjee,
• Joyita Roy,
• Kunal Roy,
• Giannis Savvas,
• Haralambos Sarimveis,
• Ewelina Wyrzykowska,
• Mateusz Balicki,
• Tomasz Puzyn,
• Georgia Melagraki,
• Iseult Lynch and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

• relationship (QSPR/QSFR) modelling, read-across, and deep learning models. Mikolajczyk et al. [16] implemented a consensus nano-QSPR scheme for the prediction of the ZP of metal oxide nanoparticles (NPs) based on the size and a quantum mechanical descriptor encoding the energy of the highest occupied molecular

• the ZP in media besides water. Wyrzykowska et al. [32] proposed a nano-QSPR model for the prediction of the ZP of 15 NPs in a low-concentration KCl solution considering the NPs’ ZP in water and the periodic number of the NPs metal. Read-across approaches presented to date include a k-nearest

Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors

• Joyita Roy and
• Kunal Roy

Beilstein J. Nanotechnol. 2024, 15, 297–309, doi:10.3762/bjnano.15.27

• them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure–property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide

• ; cell damage; MeOx NMs (metal oxide nanomaterials); nano-QSPR; zeta potential; Introduction Engineered nanoparticles have become an integral part of our daily lives in consumable products and commercial goods. Their versatile tunable properties have made nanomaterials a center of innovation in

• descriptors were calculated without any expert intervention and are independent of size variations. Splitting of the data sets Splitting of the datasets into training sets and test sets is essential for developing statistically robust nano-QSPR models. Each of the datasets, that is, the zeta potential dataset

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C₆₀ fullerene using Nano-QSPR

• Piotr Urbaszek,
• Agnieszka Gajewicz,
• Celina Sikorska,
• Maciej Haranczyk and
• Tomasz Puzyn

Beilstein J. Nanotechnol. 2017, 8, 752–761, doi:10.3762/bjnano.8.78

• number and type of substitution. The investigations have been performed with quantitative structure–property relationship modelling for nanomaterials (Nano-QSPR) – a method of defining a mathematical function that connects the structure of the investigated nanomaterial (fullerene) and the POPs (dioxin

• ) with a modeled property (energy of the PXDD@C60 complex). It is a computational technique that, to the best of our knowledge, is the first published example of the use of Nano-QSPR to predict interactions between fullerenes and numerous organic pollutants. Results Nano-QSPR model Based on the

• adsorption energy values (ΔEads) for 32 Br/Cl dibenzo-p-dioxin congeners adsorbed on a C60 fullerene surface and carefully selected structural descriptors, we developed a Nano-QSPR model, employing a hybrid genetic algorithm, partial least squares linear regression (GA-PLS), as the modeling method. The