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机器学习,虚拟查和生物活性评估以识别AJ-292/12941271作为抗繁殖剂和目标mTOR蛋白

Min Li¹, Yang Yang¹, Ran Wang¹

¹Jiangxi Provincial Key Laboratory of Drug Design and Evaluation, School of Pharmacy, Jiangxi Science & Technology Normal University, Nanchang, Jiangxi, 330013, China.

Current medicinal chemistry

|July 22, 2025

概括

研究人员开发了一种快速的方法,使用机器学习和虚拟查来发现新的mTOR抑制剂. 化合物AJ-292/12941271显示出强烈的抗癌活性和诱导的亡,为进一步研究证明安全.

科学领域:

药用化学医学化学
计算机化药物发现技术
在瘤学瘤学.

背景情况:

拉巴胺素 (mTOR) 途径的机械性标是细胞生长,增殖和存活的关键调节者.
对mTOR信号的调节失调与各种癌症有关,使mTOR成为一个重要的治疗点.
发现具有高特异性和有效性的新型mTOR抑制剂仍然是癌症药物开发中的关键挑战.

研究的目的:

开发一种快速而准确的方法来识别针对mTOR的新型抑制剂.
用虚拟查,动态模拟和生物活性评估来发现抑制剂.
评估潜在的mTOR抑制剂的抗增殖和亡作用.

主要方法:

采用ROC引导的机器学习来选一个大型复合库,识别了大约9000名潜在候选人.
对45个化合物进行虚拟选,随后进行成功识别,产生6个有前途的化合物.
进行了MTT测定,酶抑制测定,AO染色,JC-1测定和血解毒性评估,以评估生物活性和安全性.

主要成果:

六种已识别的化合物对mTOR的敏感性比PI3K更强.
化合物AJ-292/12941271和AG-205/12550019对mTOR表现出优异的活性 (IC50:分别为2.55 μM和4.48 μM).
化合物AJ-292/12941271对A549细胞系 (IC50:4.3μM) 显示出显著的抗增殖作用,诱导细胞亡,并在体内研究中显示出安全性.

结论:

结合基于ROC的机器学习,虚拟查和生物活性评估的综合方法可以快速准确地发现新型mTOR抑制剂.
化合物AJ-292/12941271是作为针对mTOR的抗癌治疗药物进一步开发的有希望的候选药物.
这种方法提供了一个精简的管道,以加快针对癌症点的药物发现工作.

关键词:

机器学习是机器学习.它们是反扩散的.生物活性.生物活性.这是一种mTOR抑制剂.虚拟选虚拟选虚拟选

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