Quantitative structure-activity relationships (QSAR) is a technique that is used to produce a model that connects biological activities of compounds to their chemical structures, and molecular docking is a technique that reveals the binding mode and interactions between a drug and its target enzyme. These techniques have been successfully applied in the design and development of many drug candidates and herein were employed to build a model that could help in the development of more potent antimalaria drugs. Descriptors of the compounds were calculated using the PaDEL-Descriptor software, and Genetic Function Algorithm (GFA) was used to select descriptors and build the model. A robust and reliable model was generated and validated to have internal and external squared correlation coefficient (R2) of 0.9622 and 0.8191, respectively, adjusted squared correlation coefficient (Radj) of 0.9471, and leave-one-out (LOO) cross-validation coefficient (Q2cv) of 0.9223. The model revealed that the antiplasmodial activities of 1,2,4,5-tetraoxane-8-aminoquinoline hybrids depend on MATS3m, GATS8p, GATS8i, and RDF50s descriptors. MATS3m, GATS8i, and RDF50s influenced the antiplasmodial activities of the compounds positively while GATS8p negatively with the greatest influence. The docking result shows strong interactions between 1,2,4,5-tetraoxane-8-aminoquinoline hybrids and Plasmodium falciparum lactate dehydrogenase (pfLDH) with binding affinities ranging from − 6.3 to − 10.9 kcal/mol which were better than that of chloroquine (− 6.1 kcal/mol), suggesting that these compounds could be better inhibitors of pfLDH than chloroquine. The results of this study could serve as a model for designing new potent 1,2,4,5-tetraoxane-8-aminoquinolines with better antiplasmodial activities for the development of highly active antimalaria drugs.

中文翻译：

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对恶性疟原虫有活性的四恶烷-8-氨基喹啉杂合体的计算机模拟

定量构效关系（QSAR）是一种用于生成将化合物的生物活性与其化学结构联系起来的模型的技术，分子对接是一种揭示药物与其靶酶之间的结合模式和相互作用的技术。这些技术已成功应用于许多候选药物的设计和开发，并在此被用来构建一个模型，有助于开发更有效的抗疟疾药物。使用PaDEL-Descriptor软件计算化合物的描述符，并使用遗传函数算法（GFA）选择描述符并建立模型。生成并验证了稳健可靠的模型，其内部和外部平方相关系数 (R2) 分别为 0.9622 和 0.8191，调整后的平方相关系数 (Radj) 为 0.9471，留一法 (LOO) 交叉验证系数 (Q2cv) 为 0.9223。该模型显示 1,2,4,5-四恶烷-8-氨基喹啉杂种的抗疟原虫活性取决于 MATS3m、GATS8p、GATS8i 和 RDF50s 描述符。MATS3m、GATS8i 和 RDF50s 对化合物的抗疟原虫活性产生积极影响，而 GATS8p 对化合物的抗疟原虫活性影响最大。对接结果显示 1,2,4,5-四恶烷-8-氨基喹啉杂合体与恶性疟原虫乳酸脱氢酶 (pfLDH) 之间的相互作用强，结合亲和力范围为 - 6.3 至 - 10.9 kcal/mol，优于氯喹（ − 6.1 kcal/mol)，表明这些化合物可能是比氯喹更好的 pfLDH 抑制剂。