Department of Toxicology and Bromatology, Faculty of Pharmacy, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, A. Jurasza 2 Street, PL-85089 Bydgoszcz, Poland.
Department of Medical Biology and Biochemistry, Faculty of Medicine, L. Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Karłowicza 24 Street, PL-85092 Bydgoszcz, Poland.
Int J Mol Sci. 2022 May 4;23(9):5132. doi: 10.3390/ijms23095132.
An approach using multivariate adaptive regression splines (MARSplines) was applied for quantitative structure-activity relationship studies of the antitumor activity of anthrapyrazoles. At the first stage, the structures of anthrapyrazole derivatives were subjected to geometrical optimization by the AM1 method using the Polak-Ribiere algorithm. In the next step, a data set of 73 compounds was coded over 2500 calculated molecular descriptors. It was shown that fourteen independent variables appearing in the statistically significant MARS model (i.e., descriptors belonging to 3D-MoRSE, 2D autocorrelations, GETAWAY, burden eigenvalues and RDF descriptors), significantly affect the antitumor activity of anthrapyrazole compounds. The study confirmed the benefit of using a modern machine learning algorithm, since the high predictive power of the obtained model had proven to be useful for the prediction of antitumor activity against murine leukemia L1210. It could certainly be considered as a tool for predicting activity against other cancer cell lines.
采用多元自适应回归样条（MARSplines）方法对蒽并吡唑类化合物的抗肿瘤活性进行定量构效关系研究。在第一阶段，使用 Polak-Ribiere 算法通过 AM1 方法对蒽并吡唑衍生物的结构进行几何优化。在下一步中，对 73 种化合物的数据集进行了 2500 多个计算分子描述符的编码。结果表明，在统计学上显著的 MARS 模型中出现了十四个独立变量（即 3D-MoRSE、2D 自相关、GETAWAY、负担特征值和 RDF 描述符的描述符），这些变量显著影响了蒽并吡唑类化合物的抗肿瘤活性。研究证实了使用现代机器学习算法的好处，因为所得到的模型具有较高的预测能力，这对于预测对小鼠白血病 L1210 的抗肿瘤活性非常有用。它无疑可以被认为是预测对其他癌细胞系活性的工具。
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