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.

背景：蒙特卡洛方法在各种科学研究中都有广泛的应用。对于以定量结构-性质/活动关系（QSPR / QSAR）形式开发预测模型，蒙特卡洛方法也可能有用。CORAL软件提供了蒙特卡洛计算，旨在建立针对不同端点的QSPR / QSAR模型。

方法：分子描述符是各种分子特征的所谓相关权重的数学函数。相关权重的数值给出目标函数的最大值。目标函数导致可见训练集的端点与最佳描述符之间的相关性。该模型的预测潜力可通过验证集进行估算，即验证模型建立过程中不涉及的化合物。

结果：该方法为大量的各种物理化学，生物化学，生态学和医学终点提供了很好的模型。本综述收集了几种CORAL模型的参考书目和基本统计​​特征。此外，还展示了该方法的扩展版本，用于更复杂的系统（纳米材料和肽），其中系统的行为由除分子结构以外的一组条件定义。

结论：可通过CORAL软件获得的蒙特卡洛技术可作为QSPR / QSAR分析的有用且便捷的工具。