The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR.
CORAL software
QSPR/QSAR
index of ideality of correlation
monte carlo method
optimal descriptors
physicochemical.
Journal
Current computer-aided drug design
ISSN: 1875-6697
Titre abrégé: Curr Comput Aided Drug Des
Pays: United Arab Emirates
ID NLM: 101265750
Informations de publication
Date de publication:
2020
2020
Historique:
received:
11
10
2018
revised:
15
02
2019
accepted:
19
03
2019
pubmed:
29
3
2019
medline:
7
4
2021
entrez:
29
3
2019
Statut:
ppublish
Résumé
The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.
Sections du résumé
BACKGROUND
BACKGROUND
The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.
METHODS
METHODS
Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.
RESULTS
RESULTS
The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.
CONCLUSION
CONCLUSIONS
The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.
Identifiants
pubmed: 30919781
pii: CAD-EPUB-97607
doi: 10.2174/1573409915666190328123112
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
197-206Informations de copyright
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