使用机器学习发现了新的SOS1抑制剂
Lihui Duo1, Yi Chen2,3, Qiupei Liu1,2
1Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 P. R. China Jianfeng.Ren@nottingham.edu.cn Bencan.Tang@nottingham.edu.cn.
RSC medicinal chemistry
|April 26, 2024
概括
机器学习为RAS驱动的癌症发现了新的SOS1抑制剂. 九种具有独特结构的化合物表现出强大的抑制作用,其中CL01545365显示出有希望的药物样性质用于治疗开发.
科学领域:
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
背景情况:
- 过度激活RAS信号通路导致30%的人类癌症.
- 七无之子1 (SOS1) 是RAS激活的关键调节剂,使其成为RAS驱动恶性瘤的治疗标.
研究的目的:
- 通过基于机器学习的虚拟查来识别SOS1的新型小分子抑制剂.
- 探索癌症治疗中SOS1抑制的新化学支架.
主要方法:
- 使用随机森林回归器对大型化合物库 (L系列,EGFR相关和CNCL) 进行虚拟选.
- 使用KRAS G12C/SOS1蛋白质与蛋白质相互作用 (PPI) 试验评估的抑制活性.
- 进行了分子对接和in silico药物相似性评估.
主要成果:
- 发现了9种新型化合物,具有尚未探索的化学框架,表现出SOS1抑制活性.
- 最强效的化合物实现了50%以上的抑制,IC50接近20μg mL-1.1.
- 目标化合物,如CL01545365,与已知的抑制剂不同,与目标内的独特口袋结合.
结论:
- 从炭酸系列中确定了具有独特结合方式的新型SOS1抑制剂.
- 这些化合物代表了开发新疗法治疗RAS驱动的癌症的有希望的候选人.
- 这些发现突显了ML驱动的VS在发现新药支架方面的潜力.
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