The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

Toropova, Alla P.; Toropov, Andrey A.; Roncaglioni, Alessandra; Benfenati, Emilio

doi:10.3390/toxics11050419

Cited by 2 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system of self-consistent models [11,13,14] for five random splits into the training (visible) and validation (invisible) sets confirms the good predictive potential of the models.…”

Section: The System Of Self-consistent Modelsmentioning

confidence: 53%

“…The system of self-consistent models [ 11 , 13 , 14 ] for five random splits into the training (visible) and validation (invisible) sets confirms the good predictive potential of the models. The training set here is divided into active and passive training and calibration sets.…”

Section: Methodsmentioning

confidence: 66%

“…The involvement of the correlation ideality index somewhat complicates the stochastic Monte Carlo optimization process. However, this complication is justified here and in other works where the mentioned index was used [ 11 , 12 , 13 , 14 , 17 ].…”

Section: Discussionmentioning

confidence: 94%

“…The simplified molecular input-line entry system (SMILES) is a representation of the molecular structure [ 7 ]. The optimal descriptor is a sum of correlation weights of SMILES atoms for eye irritation, calculated by the Monte Carlo method described in our previous work [ 11 ]:

…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Semi-Correlations for Building Up a Simulation of Eye Irritation

Toropov,

Toropova,

Roncaglioni

et al. 2023

Toxics

Self Cite

View full text Add to dashboard Cite

The OECD recognizes that data on a compound’s ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint.

show abstract

“…The system of self-consistent models [11,13,14] for five random splits into the training (visible) and validation (invisible) sets confirms the good predictive potential of the models.…”

Section: The System Of Self-consistent Modelsmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 94%

…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Semi-Correlations for Building Up a Simulation of Eye Irritation

Toropov,

Toropova,

Roncaglioni

et al. 2023

Toxics

Self Cite

View full text Add to dashboard Cite

show abstract

QSPR in molecular spaces: ordering data, {de- & re-} constructing molecular similarity matrices, building their isometric vectors defining statistical-like momenta of molecular polyhedra, and analyzing the structure of a quantum QSPR operator

Carbó-Dorca

2023

J Math Chem

View full text Add to dashboard Cite

A general review of quantum molecular similarity structure and applications is presented. The backbone of the discussion corresponds to the general problem of the data structure associated with the mathematical representation of a molecular set. How to standardize, and how to compare it to any other problem. This computational track describes the exact isometric vectors of the similarity matrix in a Minkowskian space. The further aim is to construct a set of origin-shifted vectors forming the vertices of a molecular polyhedron. From here, one can calculate a set of statistical-like momenta, providing a set of scalars that describe in a compact form the attached molecular set. Finally, the definition of a quantum QSPR operator permits building up a system of equations that can be further employed to determine the unknown properties of molecules in the original set. This last achievement leads to a quantum QSPR algorithm comparable with the classical QSPR counterpart but described in molecular space, not parameter space.

show abstract

The System of Self-Consistent Models: QSAR Analysis of Drug-Induced Liver Toxicity

Cited by 2 publications

References 41 publications

Semi-Correlations for Building Up a Simulation of Eye Irritation

Semi-Correlations for Building Up a Simulation of Eye Irritation

QSPR in molecular spaces: ordering data, {de- & re-} constructing molecular similarity matrices, building their isometric vectors defining statistical-like momenta of molecular polyhedra, and analyzing the structure of a quantum QSPR operator

Contact Info

Product

Resources

About