The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
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: 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: 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: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:16 |
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| Enthalten in: |
Current computer-aided drug design - 16(2020), 3 vom: 04., Seite 197-206 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Toropov, Andrey A [VerfasserIn] |
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| Links: |
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| Themen: |
CORAL software |
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| Anmerkungen: |
Date Completed 05.04.2021 Date Revised 05.04.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.2174/1573409915666190328123112 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a published: Print | ||
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| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a CONCLUSION: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Review | |
| 650 | 4 | |a CORAL software | |
| 650 | 4 | |a QSPR/QSAR | |
| 650 | 4 | |a index of ideality of correlation | |
| 650 | 4 | |a monte carlo method | |
| 650 | 4 | |a optimal descriptors | |
| 650 | 4 | |a physicochemical. | |
| 700 | 1 | |a Toropova, Alla P |e verfasserin |4 aut | |
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