高透率的表型查和机器学习方法使得广泛的低毒性抗松胺剂的选择成为可能
Pasquale Linciano1, Antonio Quotadamo1, Rosaria Luciani1
1Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125 Modena, Italy.
Journal of medicinal chemistry
|November 3, 2023
概括
研究人员发现了新的抗感染化合物,具有广泛的抗寄生虫和结核病活性. 机器学习指导了为进一步开发选择具有低毒性的有前途的化合物 (40).
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 寄生虫学的寄生虫学
背景情况:
- 高通量查 (HTS) 对于识别新型抗感染剂至关重要.
- 对寄生虫疾病和结核病的现有治疗方法经常面临耐药性和毒性方面的挑战.
- Ty-Box库包含用于药物发现的多种化合物.
研究的目的:
- 从Ty-Box库中识别广泛的抗感染药物.
- 利用机器学习进行化合物表征和标识.
- 评估选定化合物的抗寄生虫活性和毒性.
主要方法:
- 对456种化合物进行了高通量表型选.
- 采用机器学习方法用于化合物表征和预测建模.
- 合成和表征了44种具有广泛抗寄生虫活性和低毒性的化合物.
主要成果:
- 鉴定出44种具有广泛作用的化合物,对抗三胞体,莱什曼菌和结核菌菌.
- 化合物40具有N- ((5-pyrimidinyl) -benzenesulfonamide支架,成为一个有前途的.
- 化合物40对两种寄生虫具有较低的微分子活性,毒性最小.
结论:
- 化学信息学和机器学习工具有效地实现了有前途的抗感染药物候选药物的选择.
- 化合物40代表了一种新的,有可能进一步优化对抗寄生虫感染.
- 这项研究强调了综合查和计算方法在药物发现中的力量.
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