The Monte Carlo Method as a tool to build up predictive QSPR/QSAR.

BACKGROUND The Monte Carlo method has a wide applying 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 maximal value of a target function. The target function leads to correlation between endpoint and optimal descriptor for 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 large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models 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.

• Publication year

  2019

• Venue

  Current Computer - Aided Drug Design

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Chemistry, Computer Science

• Identifiers
  DOI 10.2174/1573409915666190328123112PMID 30919781
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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