Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

2019 ◽  
Vol 19 (29) ◽  
pp. 2643-2657 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

RSC Advances ◽  
2020 ◽  
Vol 10 (35) ◽  
pp. 20862-20871
Author(s):  
Guoyan Ren ◽  
He Sun ◽  
Gen Li ◽  
Jinling Fan ◽  
Lin Du ◽  
...  

The mechanism of interaction between AE and trypsin was studied firstly. The biological activity of both decreased after the interaction. These results provide a basis for the development and utilization of AE.


Author(s):  
Andrey A. Toropov ◽  
Alla P. Toropova ◽  
Emilio Benfenati ◽  
Orazio Nicolotti ◽  
Angelo Carotti ◽  
...  

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.


2017 ◽  
Vol 14 (3) ◽  
pp. 597-610
Author(s):  
Baghdad Science Journal

New complexes of the [M(Ura)(Phen)(OH2)Cl2]Cl.2H2O type, where (Ura) uracil ; (Phen) 1,10-phenanthroline hydrate; M (Cr+3 , Fe+3 and La+3) were synthesized from mix ligand and characterized . These complexes have been characterized by the elemental micro analysis, spectral (FT-IR., UV-Vis, 1HNMR, 13CNMR and Mass) and magnetic susceptibility as well the molar conductive mensuration. Cr+3, Fe+3 and La+3- complexes of six–coordinated were proposed for the insulated for three metal(III) complexes for molecular formulas following into uracil property and 1,10-phenanthroline hydrate present . The proposed molecular structure for all metal (III) complexes is octahedral geometries .The biological activity was tested of metal(III) salts, ligands as well as metal(III) complexes to the pathogenic bacteria as well as the antifungal activity has been studied .


2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Syed Ajaz K. Kirmani ◽  
Parvez Ali ◽  
Faizul Azam ◽  
Parvez Ahmad Alvi

The design of the quantitative structure-property/activity relationships for drug-related compounds using theoretical methods relies on appropriate molecular structure representations. The molecular structure of a compound comprises all the information required to determine its chemical, biological, and physical properties. These properties can be assessed by employing a graph theoretical descriptor tool widely known as topological indices. Generalization of descriptors may reduce not only the number of molecular graph-based descriptors but also improve existing results and provide a better correlation to several molecular properties. Recently introduced ve-degree and ev-degree topological indices have been successfully employed for development of models for the prediction of various biological activities/properties. In this article, we propose the general ve-inverse sum indeg index ISI α , β ve G and general ve-Zagreb index M α ve G of graph G and compute ISI α , β ve G , M α ve G , and M α ev G (general ev-degree index) of hyaluronic acid-curcumin/paclitaxel conjugates, renowned for its potential anti-inflammatory, antioxidant, and anticancer properties, by using molecular structure analysis and edge partitioning technique. Several ve-degree- and ev-degree-based topological indices are obtained as a special case of ISI α , β ve G , M α ve G , and M α ev G . Furthermore, QSPR analysis of ISI α , β ve G , M α ve G , and M α ev G for particular values of α and β is performed, which reveals their predicting power. These results allow researchers to better understand the physicochemical properties and pharmacological characteristics of these conjugates.


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