Succinimides, which contain a pharmacophore responsible for anticonvulsant activity, are frequently used antiepileptic drugs and the synthesis of their new derivatives with improved efficacy and tolerability presents an important task. Nowadays, multitarget/tasking methodologies focused on quantitative-structure activity relationships (mt-QSAR/mtk-QSAR) have an important role in the rational design of drugs since they enable simultaneous prediction of several standard measures of biological activities at diverse experimental conditions and against different biological targets. Relating to this very topic, the mt-QSAR/mtk-QSAR methodology can give only binary classification models, and as such, in this study a regression mtk-QSAR (rmtk-QSAR) model based on a novel modular neural network (MNN) has been proposed. The MNN uses standard classification mtk-QSAR models as input modules, while the regression is performed by the output module. The rmtk-QSAR model has been successfully developed for the simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides, with a satisfactory accuracy in testing (R² = 0.87). Thus, the proposed mtk-QSAR regression method can be regarded as a viable alternative to the standard QSAR methodology.

中文翻译：

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从分类到回归，使用新型模块化神经网络进行多任务QSAR建模：琥珀酰亚胺抗惊厥活性和神经毒性的同时预测

琥珀酰亚胺包含负责抗惊厥活性的药效基团，是常用的抗癫痫药，合成具有改善的功效和耐受性的新衍生物是一项重要的任务。如今，专注于定量结构活性关系（mt-QSAR / mtk-QSAR）的多目标/任务方法论在药物的合理设计中起着重要作用，因为它们可以同时预测多种实验条件下生物活性的几种标准量度，并针对不同的生物学目标。与此主题相关，mt-QSAR / mtk-QSAR方法只能提供二进制分类模型，因此，在本研究中，基于新型模块化神经网络（MNN）的回归mtk-QSAR（rmtk-QSAR）模型已经提出。MNN使用标准分类mtk-QSAR模型作为输入模块，而回归则由输出模块执行。已成功开发了rmtk-QSAR模型，用于同时预测琥珀酰亚胺的抗惊厥活性和神经毒性，并且在测试中具有令人满意的准确性（R ²＝ 0.87）。因此，可以将提出的mtk-QSAR回归方法视为标准QSAR方法的可行替代方法。