通过基于混合AI结构的方法发现双重活性抗癌化合物
Michele Roggia1, Benito Natale1, Giorgio Amendola1
1DiSTABiF, Università della Campania Luigi Vanvitelli, Via Vivaldi 43, Caserta 81100, Italy.
Journal of chemical information and modeling
|September 14, 2024
概括
这项研究开发了一种计算方法,以寻找针对G-四重复 (G4) 结构和多ADP-核糖) 聚合酶 (PARP) 的双重作用抗癌药物1. 四种化合物表现出双重活性,并抑制了癌细胞生长.
科学领域:
- 在瘤学瘤学.
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 癌细胞通过保持端粒长度和耐受DNA损伤来逃避死亡.
- 针对端粒维护和DNA修复途径提供了一个强大的抗癌策略.
- 在癌症治疗中,G-四重复 (G4) 结构和PARP1是癌症治疗的关键标.
研究的目的:
- 通过计算识别具有对G4结构和PARP1.1双重抑制活性的新型化合物.
- 开发一种混合虚拟选方法,将AI和基于结构的方法结合起来.
- 为了验证化合物的抗癌功效.
主要方法:
- 使用混合虚拟选 (VS) 协议集成机器学习 (PyRMD) 和基于结构的VS.
- 人工智能驱动的分析从大型化学图书馆中确定了潜在的PARP1抑制剂.
- 基于结构的VS优先抑制G4稳定潜力的抑制剂.
主要成果:
- 通过两步VS过程确定了50个有前途的候选化合物.
- 实验验证证了化合物的双重PARP1抑制和G4稳定.
- 四种化合物对癌症细胞系表现出显著的抗增殖作用.
结论:
- 结合人工智能和基于结构的方法,有效地发现多目标抗癌剂.
- 双重向化合物为克服癌症抵抗提供了一个有希望的策略.
- 这种方法加速了新型癌症治疗方法的开发.
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