The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES

Toropova, Alla P.; Toropov, Andrey A.

doi:10.1016/j.scitotenv.2024.172119

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3. Improving the algorithm of Monte Carlo optimization via the use of new statistical criteria [62]. In addition, quasi-SMILES can be a suitable representation for polymers [37] and nanomaterials [63].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Toropova,

Toropov,

Roncaglioni

et al. 2024

Macromol

Self Cite

View full text Add to dashboard Cite

The inhibitory activity of peptides on angiotensin-converting enzyme (ACE) is a measure of their antihypertensive potential. Quantitative structure–activity relationship (QSAR) models obtained based on the analysis of sequences of amino acids are suggested. The average determination coefficient for the active training sets is 0.36 ± 0.07. The average determination coefficient for validation sets is 0.79 ± 0.02. The paradoxical situation is caused by applying the vector of ideality of correlation, which improves the statistical quality of a model for the calibration and validation sets but is detrimental to the statistical quality of models for the training sets.

show abstract

Section: Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Toropova,

Toropov,

Roncaglioni

et al. 2024

Macromol

Self Cite

View full text Add to dashboard Cite

show abstract

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles

Wang,

Huang,

Zhang

et al. 2025

Chemosphere

View full text Add to dashboard Cite

In Silico Simulation of Daphnia magna Immobilization Exposed to Mixtures of TiO2 Nanoparticles with Inorganic Compounds

Toropova,

Toropov,

Roncaglioni

et al. 2025

J. Compos. Sci.

View full text Add to dashboard Cite

The development of models of the physicochemical and biochemical behavior of nanomaterials is useful for improving the evaluation and management of this material. Quasi-SMILES technology makes it possible to quite successfully cope with this kind of modeling task, accounting for various experimental conditions, where the use of other approaches is difficult or even impossible. Here, we describe the results of using quasi-SMILES to model the toxicity of mixtures of titanium nano oxide with various inorganic substances towards Daphnia magna. The approach is based on the stochastic process of the optimization of correlation weights for different codes used in quasi-SMILES. The optimization process was carried out using special statistical criteria for predictive potential. It is shown that models built using quasi-SMILES have the best predictive potential.

show abstract

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES

Cited by 3 publications

References 36 publications

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

Machine learning models for quantitatively prediction of toxicity in macrophages induced by metal oxide nanoparticles

In Silico Simulation of Daphnia magna Immobilization Exposed to Mixtures of TiO2 Nanoparticles with Inorganic Compounds

Contact Info

Product

Resources

About