Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials

The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions

The different features of the impact of nanoparticles on cells, such as the structure of the core, presence/absence of doping, quality of surface, diameter, and dose, were used to define quasi-SMILES, a line of symbols encoded the above physicochemical features of the impact of nanoparticles. The correlation weight for each code in the quasi-SMILES has been calculated by the Monte Carlo method. The descriptor, which is the sum of the correlation weights, is the basis for a one-variable model of the biological activity of nano-inhibitors of human lung carcinoma cell line A549. The system of models obtained by the above scheme was checked on the self-consistence, i.e., reproducing the statistical quality of these models observed for different distributions of available nanomaterials into the training and validation sets. The computational experiments confirm the excellent potential of the approach as a tool to predict the impact of nanomaterials under different experimental conditions. In conclusion, our model is a self-consistent model system that provides a user to assess the reliability of the statistical quality of the used approach.

Quasi-SMILES as a basis to build up models of endpoints for nanomaterials

Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model's predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.

Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity

The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).

Development of Self-Consistency Models of Anticancer Activity of Nanoparticles under Different Experimental Conditions Using Quasi-SMILES Approach

Algorithms of the simulation of the anticancer activity of nanoparticles under different experimental conditions toward cell lines A549 (lung cancer), THP-1 (leukemia), MCF-7 (breast cancer), Caco2 (cervical cancer), and hepG2 (hepatoma) have been developed using the quasi-SMILES approach. This approach is suggested as an efficient tool for the quantitative structure-property-activity relationships (QSPRs/QSARs) analysis of the above nanoparticles. The studied model is built up using the so-called vector of ideality of correlation. The components of this vector include the index of ideality of correlation (IIC) and the correlation intensity index (CII). The epistemological component of this study is the development of methods of registration, storage, and effective use of experimental situations that are comfortable for the researcher-experimentalist in order to be able to control the physicochemical and biochemical consequences of using nanomaterials. The proposed approach differs from the traditional models based on QSPR/QSAR in the following respects: (i) not molecules but experimental situations available in a database are considered; in other words, an answer is offered to the question of how to change the plot of the experiment in order to achieve the desired values of the endpoint being studied; and (ii) the user has the ability to select a list of controlled conditions available in the database that can affect the endpoint and evaluate how significant the influence of the selected controlled experimental conditions is.

In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 Cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES

A simulation of the effect of metal nano-oxides at various concentrations (25, 50, 100, and 200 milligrams per millilitre) on cell viability in THP-1 cells (%) based on data on the molecular structure of the oxide and its concentration is proposed. We used a simplified molecular input-line entry system (SMILES) to represent the molecular structure. So-called quasi-SMILES extends usual SMILES with special codes for experimental conditions (concentration). The approach based on building up models using quasi-SMILES is self-consistent, i.e., the predictive potential of the model group obtained by random splits into training and validation sets is stable. The Monte Carlo method was used as a basis for building up the above groups of models. The CORAL software was applied to building the Monte Carlo calculations. The average determination coefficient for the five different validation sets was R² = 0.806 ± 0.061.