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

• the Elemental Descriptor Calculator software available from (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/other-dtc-lab-tools?authuser=0), termed first-generation periodic table descriptors. Also, second-generation PT descriptors were calculated using relevant formulas [28]. These

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

• 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

• present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties. Keywords: heavy metals; HK-2 cell; ML algorithm; periodic table descriptors; QSAR; Introduction Nanoparticles (NPs) have gained much

• AdaBoost) with periodic table descriptors for predicting the cytotoxicity, in terms of cell viability, of eight heavy metals adsorbed on nano-TiO2. Also, the best algorithm showing the most contributing features responsible for the toxicity to HK-2 (human kidney 2) cell has been determined. To the best