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

• for Sustainable Manufacturing, Monterrey 64849, Mexico Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico 10.3762/bjnano.15.95 Abstract Employing quantitative structure–activity

• 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

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

• principles of nano-quantitative structure–toxicity relationship (nano-QSTR) models. These models integrate read-across techniques with traditional quantitative structure–activity relationship (QSAR) methods to enhance the predictive capabilities, particularly in datasets with limited data points [19]. Using

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

• information regarding the toxicity and reactivity of these NPs by monitoring the behaviour of nano-descriptors commonly employed in quantitative structure–activity relationship (QSAR) models and by measuring the water–NP energetic interactions. The extracted information from our simulations complements

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

• Conectividad Molecular, Avda. Vicent Andrés Estellés 0, 46100 Burjassot, Spain MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain 10.3762/bjnano.15.71 Abstract Quantitative structure–activity relationship (QSAR) models are routinely used to predict the properties and biological activity

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

• this does not contribute to the variations in zeta potential values due to solvents. This work is similar to imputation in quantitative structure–activity relationship (QSAR) modeling, where a missing value is replaced by a predicted value from another model [30]. The training set with 111 MeOx NPs

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

• weaknesses of the resources are discussed along with possible future developments. Keywords: databases; ISA-TAB-Nano; nanoinformatics; nanotoxicology; quantitative structure–activity relationship (QSAR); Introduction Nanotechnology, which may be considered the design and application of engineered