Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy.
Chem Biol Drug Des. 2011 Jun;77(6):471-6. doi: 10.1111/j.1747-0285.2011.01117.x. Epub 2011 May 4.
CORrelations And Logic (coral at http://www.insilico.eu/coral) is freeware aimed at establishing a quantitative structure - property/activity relationships (QSPR/QSAR). Simplified molecular input line entry system (SMILES) is used to represent the molecular structure. In fact, symbols in SMILES nomenclatures are indicators of the presence of defined molecular fragments. By means of the calculation with Monte Carlo optimization of the so called correlation weights (contributions) for the above-mentioned molecular fragments, one can define optimal SMILES-based descriptors, which are correlated with an endpoint for the training set. The predictability of these descriptors for an external validation set can be estimated. A collection of SMILES-based models of anticancer activity of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines for different splits into training and validation set which are calculated with the coral are examined and discussed. Good performance has been obtained for three splits: the r(2) ranged between 0.778 and 0.829 for the sub-training set, between 0.828 and 0.933 for the calibration set, and between 0.807 and 0.931 for the validation set.
CORrelations And Logic (coral at http://www.insilico.eu/coral) 是一款免费软件,旨在建立定量结构-性质/活性关系(QSPR/QSAR)。简化分子输入线(entry system,SMILES) 用于表示分子结构。事实上,SMILES 命名法中的符号是定义分子片段存在的指标。通过使用蒙特卡罗优化所谓的相关权重(贡献)对上述分子片段进行计算,可以定义与训练集终点相关的最佳 SMILES 基描述符。可以估计这些描述符对外部验证集的可预测性。对不同拆分的 1,4-二氢-4-氧代-1-(2-噻唑基)-1,8-萘啶类化合物抗癌活性的基于 SMILES 的珊瑚模型进行了检查和讨论。对于三个拆分,均获得了良好的性能:子训练集的 r(2) 范围在 0.778 到 0.829 之间,校准集的 r(2) 范围在 0.828 到 0.933 之间,验证集的 r(2) 范围在 0.807 到 0.931 之间。