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Pattern Recognition Techniques Applied to the Study of Leishmanial Glyceraldehyde-3-Phosphate Dehydrogenase Inhibition

机译:模式识别技术在利什曼醛甘油醛-3-磷酸脱氢酶抑制研究中的应用

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摘要

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.
机译:为了获得结构-活性关系(SAR)模型,使用了化学计量模式识别技术,该模型将一系列腺苷化合物的结构与对墨西哥利什曼原虫的甘油醛3-磷酸脱氢酶(LmGAPDH)的亲和力相关。使用了49种化合物的训练集建立模型,并用一个几何和四个电子描述符获得了最佳化合物。分类模型通过预测模型构建过程中未使用的14种化合物的测试集从外部验证。通过正确的分类验证,获得了高质量的结果。此外,该结果与先前对这些分子的SAR研究非常吻合,以至于我们可以建议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。

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