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使用基于结构的药模型,机器学习和分子动态模拟来计算发现新型PI3KC2α抑制剂

Bana Katrib¹, Ahmed Adel¹, Mohammed Abadleh¹

¹Faculty of Pharmacy and Medical Sciences, University of Petra, Amman, 11196, Jordan.

Journal of molecular graphics & modelling

|March 20, 2025

概括

研究人员使用机器学习和模拟开发了一种新的计算方法,以找到PI3KC2α的抑制剂,PI3KC2α是一种与癌症转移有关的蛋白质. 一个已识别的化合物显示出有希望的结合稳定性和亲和力.

科学领域:

生物化学生物化学
计算化学的计算化学
药理学药理学是指药理学的学科.

背景情况:

酸酸3-酶C2α (PI3KC2α) 涉及到癌症转移和血栓形成.
向PI3KC2α提供了治疗机会,但抑制剂是有限的,特别是对于结构数据稀缺的激酶.

研究的目的:

引入一种新的计算工作流程,用于发现新的PI3KC2α抑制剂.
通过数据增强和多样化的药物生成,增强机器学习模型的稳定性.

主要方法:

整合基于结构的药模拟,机器学习 (XGBoost) 和分子动力学模拟.
从结晶学和对接数据生成药,并使用连接体构造样本进行增强.
在NCI数据库的虚拟选中,使用了优化的药模型.

主要成果:

一个最佳的机器学习模型确定了四个关键的药理和三个控制生物活性的描述.
虚拟查从NCI数据库中发现了三种潜在的PI3KC2α抑制剂.
希特H_1 (NCI: 725847) 的结合稳定性和亲和力与已知的强抑制剂 (PITCOIN1,IC50 = 95 nM) 相似.

结论:

本研究介绍了第一个对PI3KC2α抑制的构造增强机器学习框架.
开发的工作流提供了针对具有有限结构信息的激酶的可行策略.
鉴定到的成功化合物值得进一步调查,因为它是潜在的PI3KC2α抑制剂.

关键词:

数据增强数据增强机器学习机器学习分子动态模拟分子动态模拟这就是PI3KC2α.基于结构的药模拟.

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