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The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR
Andrey A Toropov1, Alla P Toropova1
1Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, 20156 Milan, Italy.
Current Computer-Aided Drug Design
|March 29, 2019
Summary
The CORAL software utilizes Monte Carlo methods to build predictive quantitative structure-property/activity relationship (QSPR/QSAR) models. This approach effectively models diverse endpoints, offering a convenient tool for scientific research.
Area of Science:
- Computational chemistry
- Cheminformatics
- Predictive modeling
Background:
- The Monte Carlo method is widely applied across scientific research.
- Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) are crucial for predictive modeling.
- The CORAL software facilitates Monte Carlo calculations for QSPR/QSAR model development.
Purpose of the Study:
- To demonstrate the utility of the Monte Carlo approach via CORAL software for QSPR/QSAR analysis.
- To review existing CORAL models and their statistical characteristics.
- To present an extended CORAL approach for complex systems.
Main Methods:
- Utilizing molecular descriptors derived from correlation weights of molecular features.
- Employing a target function to correlate endpoints with optimal descriptors on a training set.
- Validating model predictive potential using an independent validation set.
Main Results:
- Development of effective QSPR/QSAR models for various physicochemical, biochemical, ecological, and medicinal endpoints.
- Compilation of a bibliography and statistical data for CORAL models.
- Demonstration of an extended CORAL version for complex systems like nanomaterials and peptides.
Conclusions:
- The Monte Carlo technique, implemented in CORAL software, serves as a valuable tool for QSPR/QSAR analysis.
- The CORAL approach is effective for a wide range of endpoints.
- The software is adaptable for more complex scientific systems.
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