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从FDA批准的药物中识别潜在的酸酶抑制剂,使用机器学习,分子对接和实验验证

Muhammad Yasir¹, Jinyoung Park¹, Eun-Taek Han²

¹Department of Pharmacology, Kangwon National University School of Medicine, Chuncheon, 24341, Republic of Korea.

|September 23, 2024

概括

机器学习确定了FDA批准的药物,包括兰迪诺洛,兰纳米尼维尔和cidofovir,作为有效的酶抑制剂. 这种方法加速了对炎症和过敏疾病的药物发现.

科学领域:

生物化学生物化学
药理学药理学是指药理学的学科.
计算生物学计算生物学

背景情况:

酸酶是炎症和过敏反应的关键调解剂,使其成为相关疾病的治疗点.
由于酶的复杂激活和调节,开发有效的酶抑制剂具有挑战性.
药物再利用提供了一个潜在的战略,以更快地识别新疗法.

研究的目的:

采用机器学习 (ML) 来识别FDA批准的药物中的新型酶抑制剂.
通过分子对接和实验分析验证ML识别的化合物.
探索ML驱动的药物重定向,用于开发酶抑制剂.

主要方法:

使用机器学习模型对FDA批准的广泛药物库进行选.
分子对接模拟以评估在酸酶活性部位内的结合亲和和形状.
使用RBL-2H3细胞测量合酶,β-hexosaminidase和基因组胺释放的抑制的实验验证.

主要成果:

一些FDA批准的药物,包括兰地,兰纳米尼维尔和cidofovir,显示出显著的酶抑制.
这些已识别的药物的疗效与已知药物,如尼多克罗米尔和APC-366.6的疗效相当.
机器学习成功预测和实验验证实了新型酸酶抑制剂.

结论:

机器学习是一个强大的工具,可以加速药物的重新用途和识别潜在的治疗方法.
经FDA批准的药物可以作为新型三酶抑制剂的宝贵来源.
这项研究提供了一种经过验证的基于ML的方法,用于发现针对酸酶相关病理的抑制剂.

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Protein-protein Interfaces

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