Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.

Graphic abstract

中文翻译：

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发现新的锥虫硫酮合成酶抑制剂的集成学习应用

由锥虫引起的疾病是疾病负担最重的被忽视的传染病之一，影响着全世界约 2700 万人，尤其是社会经济弱势群体。锥虫硫酮合成酶 (TryS) 被认为是 typanosomatids 的硫醇-多胺代谢中最具吸引力的药物靶点之一，具有独特性、必要性和可成药性。在这里，我们编译了一个 401 T 的数据集。布鲁塞TryS 抑制剂包括具有文献中报道的抑制数据的化合物，也包括内部获得的数据。QSAR 分类器是从这些数据集派生和验证的，使用公开可用的开源软件，从而确保所获得模型的可移植性。通过集成学习，所得模型的性能和鲁棒性得到了显着提高。通过回顾性虚拟筛选活动进一步评估了单个模型和模型集合的性能。最后，作为一个应用示例，所选的模型集成已应用于 DrugBank 5.1.6 化合物库的前瞻性虚拟筛选活动。所有的内部 本研究中使用的脚本可应要求提供，而数据集已作为补充材料包含在内。

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