应用以数据为中心的监督机器学习来预测Trypanosoma cruzi临床相关阶段的表型活动
Nicolás Pérez-Mauad1,2, Lucas N Alberca3, Alejandra C Schoijet4,5
1Instituto de Química y Metabolismo del Fármaco (IQUIMEFA), CONICET-Universidad de Buenos Aires, Buenos Aires C1113, Argentina.
Pharmaceutics
|December 31, 2025
概括
机器学习模型发现了查加斯病的新药候选者,这是一个被忽视的热带疾病. 六种化合物对Trypanosoma cruzi表现出三酸性活性,提供了潜在的新疗法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 寄生虫学的寄生虫学
背景情况:
- 查加斯病是由Trypanosoma cruzi引起的,在全球范围内影响了800万人.
- 目前的治疗方法 (二,尼福蒂莫克斯) 的疗效和副作用有限.
- 需要针对T. cruzi. 的新型治疗策略.
研究的目的:
- 使用机器学习探索用于查加斯病的药物重定向.
- 确定T. cruzi感染的潜在新疗法解决方案.
- 开发特里帕诺西德活性的预测模型.
主要方法:
- 策划了来自ChEMBL的超过10万个生物活性数据点.
- 开发了T. cruzi Y菌株的三型马斯蒂哥特和Tulahuen菌株的阿马斯蒂哥特的整体学习模型.
- 进行了对DrugBank和Drug Repurposing Hub数据库的前性选.
主要成果:
- 机器学习模型实现了高回顾性选性能 (AUROC>0.96).
- 八个in silico结果被选择用于实验验证.
- 六种化合物证明了对T. cruzi amastigotes的确认的三酸性作用.
结论:
- 开发了便携式元分类器,用于识别tripanocidal小分子.
- 通过实验验证了预测模型.
- 确定了用于治疗夏加斯病的有前途的候选药物,特别是针对临床相关的阿马斯蒂哥特阶段.
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