通过机器学习引导重新定位SARS-CoV-2-向分子序列作为Cruzain抑制剂
Rafael F Lameiro1, Luiz F Barbosa1, Evelin R Cardoso1
1Medicinal and Biological Chemistry Group, São Carlos Institute of Chemistry, University of São Paulo, Avenida Trabalhador Sancarlense, 400, São Carlos, SP, 13566-590, Brazil.
ChemMedChem
|January 31, 2026
概括
使用机器学习的药物重新定位发现了查加斯病的新抑制剂. 这种方法选了COVID-19抗病毒化合物,发现有效的cruzain抑制剂具有有利的结合特性和类似药物的特征.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 寄生虫学的寄生虫学
背景情况:
- 药物重用确定了现有药物的新用途,对查加斯病等疾病至关重要.
- 机器学习 (ML) 通过选化合物库来加速药物发现.
- 在COVID-19大流行期间开发的抗病毒化合物为其他疾病提供了潜在的资源.
研究的目的:
- 开发一种机器学习模型,用于预测针对沙加斯病的向蛋白酶cruzain的抑制活性.
- 选一套原先针对SARS-CoV-2的peptidomimetic化合物的库,Mpro和cathepsin L,用于克鲁扎因抑制.
- 确定新型,高 afinity cruzain 抑制剂,并扩大用于查加斯病治疗的化学空间.
主要方法:
- 开发一个ExtraTrees机器学习模型来预测cruzain抑制活性.
- 使用ML模型对专有二体图书馆进行针对cruzain的选.
- 使用异热定位热量计 (ITC) 验证已识别的命中化合物.
主要成果:
- 从一个针对SARS-CoV-2 Mpro和cathepsin L.的图书馆中识别出高亲密度的cruzain抑制剂.
- 在抑制剂中发现了新的P1部分,扩大了已知的化学空间.
- 选择的化合物表现出有利的和结贡献,而不会损害类似药物的特性 (例如,低脂性).
结论:
- 机器学习有效地重新使用药物库来治疗像查加斯病这样被忽视的热带疾病.
- 这项研究确定了有前途的克鲁扎因抑制剂,具有改善的结合热力学和类似药物的特性.
- 这些发现证明了利用全球健康化合物库用于各种治疗应用的价值.
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