In the present article, k nearest neighbour molecular field analysis (kNN-MFA) method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. In this study 37 derivatives of quinoxaline having antimalarial activity were used. Sphere exclusion (SE) algorithm was used to create the biological activity data set in to into training and test set. For model generation kNN-MFA method has coupled with stepwise, simulated annealing and genetic algorithm this method provides various models, in which the most significant model developed by stepwise backward-forward method with predictive internal q²=0.7589 and external predictivity (pred_r² = 0.4752). In the presented model electrostatic descriptors play crucial role for activity. Electrostatic descriptor (E_137) indicates regions in which electron withdrawing groups are favourable and descriptor (E_939) represents electron rich or electron donating groups are advantageous in particular region. The counter map/ plot of this model further helps to understand the relationship of structural feature of derivative of quinoxaline and its biological activity this would be applied for designing of new potent antimalarial containing quinoxaline as lead.

中文翻译：

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使用 K-近邻分子场分析对喹喔啉衍生物作为抗疟药进行 3D QSAR 分析

本文采用k最近邻分子场分析(kNN-MFA)方法建立三维定量构效关系(3D-QSAR)模型。在这项研究中，使用了 37 种具有抗疟活性的喹喔啉衍生物。球体排除 (SE) 算法用于将生物活性数据集创建到训练和测试集中。对于模型生成，kNN-MFA 方法与逐步、模拟退火和遗传算法相结合，该方法提供了各种模型，其中最重要的模型是由逐步后向方法开发的，预测内部 q ² =0.7589 和外部预测（pred_r ²= 0.4752）。在所呈现的模型中，静电描述符对活动起着至关重要的作用。静电描述符（E_137）表示吸电子基团有利的区域，描述符（E_939）表示富电子或给电子基团在特定区域有利。该模型的计数器图/图进一步有助于了解喹喔啉衍生物的结构特征与其生物活性之间的关系，这将用于设计以喹喔啉为铅的新型强效抗疟药。