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基于机器学习的方法开发强大的EGFR抑制剂用于乳腺癌 - 设计,合成和体外评估

Hossam Nada¹, Anam Rana Gul², Ahmed Elkamhawy^1,3

¹BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang 10326, Republic of Korea.

|September 11, 2023

概括

研究人员开发了一个人工智能工具来预测新的表皮生长因子受体 (EGFR) 抑制剂. 这导致了化合物9的发现,该化合物是乳腺癌治疗的有希望的候选物,具有强大的EGFR抑制和抗增殖活性.

科学领域:

在瘤学瘤学.
药用化学医学化学
生物信息学是一种生物信息学.

背景情况:

表皮生长因子受体 (EGFR) 在细胞功能和癌症发育中起着至关重要的作用.
瘤中的EGFR过度表达使其成为抗癌药物的特定标.
对现有的EGFR抑制剂的耐药性需要开发新的治疗药物.

研究的目的:

开发一种机器学习模型,用于预测新型EGFR抑制剂的生物活性.
设计和合成新的基于4-anilinoquinazoline的EGFR抑制剂.
为了确定潜在的乳腺癌治疗的强效EGFR抑制剂.

主要方法:

集成一个大型EGFR小分子抑制剂库,以确定有前途的支架.
合理的药物设计和基于人工智能的虚拟查,用于新型抑制剂设计.
对细胞毒性和EGFR抑制活性新型化合物的合成和评估.

主要成果:

识别了N替代的quinazolin-4-amine衍生物作为EGFR抑制剂的主要集群.
开发了一个AI应用程序来预测生物活性,指导合成化合物的选择.
合成了18种新型化合物,其中第9种化合物表现出强烈的抗增殖活性 (2.50微米对抗MCF-7,1.96微米对抗MDA-MB-231) 和EGFR抑制 (2.53nM).

结论:

化合物9表现出显著的抗增殖和EGFR抑制作用.
化合物9表明作为乳腺癌治疗的治疗候选者的潜力.
人工智能驱动的方法加速了有效EGFR抑制剂的发现.

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