Lozano Norka B H, Oliveira Rafael F, Weber Karen C, Honorio Kathia M, Guido Rafael V C, Andricopulo Adriano D, de Sousa Alexsandro G, da Silva Albérico B F
Instituto de Química de São Carlos, USP, São Carlos (SP), 13566-590, Brazil.
Universidade Federal da Paraíba, João Pessoa (PB), 58051-900, Brazil.
Int J Mol Sci. 2014 Feb 21;15(2):3186-203. doi: 10.3390/ijms15023186.
Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.
采用化学计量学模式识别技术以获得结构-活性关系(SAR)模型,该模型将一系列腺苷化合物的结构与墨西哥利什曼原虫甘油醛-3-磷酸脱氢酶(LmGAPDH)的亲和力相关联。使用一组49种化合物的训练集来构建模型,并且通过一个几何描述符和四个电子描述符获得了最佳模型。分类模型通过对在模型构建过程中未使用的14种化合物的测试集进行预测来进行外部验证。获得了高质量的结果,这通过正确的分类得到了验证。此外,这些结果与先前对这些分子的SAR研究高度一致,以至于我们可以认为这些发现可能有助于进一步研究能够改善利什曼病治疗的LmGAPDH配体。