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

• ; read-across; QSPR; round-robin test; zeta potential; Introduction Nanotechnology, defined as the ability to manipulate matter at the nanoscale, has opened an array of possibilities for multiple applications that take advantage of the unique properties of nanomaterials (NMs). From targeted drug

• 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

AI-assisted models to predict chemotherapy drugs modified with C₆₀ fullerene derivatives

• Jonathan-Siu-Loong Robles-Hernández,
• Dora Iliana Medina,
• Katerin Aguirre-Hurtado,
• Marlene Bosquez,
• Roberto Salcedo and
• Alan Miralrio

Beilstein J. Nanotechnol. 2024, 15, 1170–1188, doi:10.3762/bjnano.15.95

• relationship (QSAR)/ quantitative structure–property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug–fullerene complexes (i.e., drug–pristine C60 fullerene and drug–carboxyfullerene C60

• –COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson’s hard–soft acid–base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum

• –property relationships (QSAR/QSPR) are a paradigm that can be useful in choosing promising molecules, considering the information on inactive and active compounds, through in silico approaches. According to the QSAR/QSPR paradigm, a given activity/property, f, can be modeled using a set of quantitative

Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles

• Supratik Kar and
• Siyun Yang

Beilstein J. Nanotechnol. 2024, 15, 1142–1152, doi:10.3762/bjnano.15.93

• the years, QSAR/QSPR/QSTR techniques have been employed to establish correlations between various characteristics of nanomaterials and their toxicity [19][20][21][22][23]. Nano-quantitative read-across structure–toxicity relationship (nano-qRASTR) models are an advanced approach that builds upon the

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

• treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that

• lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence

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

• combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure–property relationship (QSPR) model (R2 = 0.998), predicting

• the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor

• (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed. Keywords: brominated; chlorinated; dioxins; fullerenes; QSPR; sorption; Introduction Dioxin congeners are