Cruzain is an established target for the identification of novel trypanocidal agents, but how good are in vitro/in vivo correlations? This work describes the development of a random forests model for the prediction of the bioavailability of cruzain inhibitors that are Trypanosoma cruzi killers. Some common properties that characterize drug‐likeness are poorly represented in many established cruzain inhibitors. This correlates with the evidence that many high‐affinity cruzain inhibitors are not trypanocidal agents against T. cruzi. On the other hand, T. cruzi killers that present typical drug‐like characteristics are likely to show better trypanocidal action than those without such features. The random forests model was not outperformed by other machine learning methods (such as artificial neural networks and support vector machines), and it was validated with the synthesis of two new trypanocidal agents. Specifically, we report a new lead compound, Neq0565, which was tested on T. cruzi Tulahuen (β‐galactosidase) with a pEC₅₀ of 4.9. It is inactive in the host cell line showing a selectivity index (SI = EC₅₀^cyto/EC₅₀^T. cruzi) higher than 50.

中文翻译：

---

随机森林辅助的分子设计和有效的锥虫杀虫剂作为克鲁萨因抑制剂的合成，用于治疗南美锥虫病

Cruzain是鉴定新型锥虫杀灭剂的既定目标，但体外/体内相关性有多好？这项工作描述了随机森林模型的发展，用于预测作为克鲁斯锥虫杀手的克鲁萨因抑制剂的生物利用度。许多已建立的克鲁赞抑制剂中都缺乏代表药物相似性的一些共同特性。这与许多高亲和力的克鲁萨因抑制剂不是针对克鲁斯锥虫的锥虫杀灭剂的证据相关。另一方面，T。cruzi具有典型药物样特征的杀手可能比没有此类特征的杀手表现出更好的锥虫杀灭作用。随机森林模型在其他机器学习方法（例如人工神经网络和支持向量机）上并没有表现出色，并且可以通过合成两种新的杀锥虫剂进行验证。具体而言，我们报告了一种新的先导化合物Neq0565，该化合物已在克鲁氏杆菌Tulahuen（β-半乳糖苷酶）上进行了测试，pEC ₅₀为4.9。在宿主细胞系中无活性，显示选择性指数（SI = EC ₅₀^细胞/ EC ₅₀ ^{T. cruzi}）高于50。