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深度学习和分子动力学揭示了针对表观遗传癌症的有希望的EZH2抑制剂

Damilola A Omoboyowa¹, Temitope C Aribigbola¹, Babasola Aiyeku²

¹Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria.

Computational biology and chemistry

|November 14, 2025

概括

研究人员使用人工智能和计算方法开发了新型EZH2抑制剂. 这些已识别的化合物表现出强烈的结合亲和力和有利的药理动力学特性,加速了药物发现.

科学领域:

药用化学医学化学
计算化学的计算化学
药物发现药物发现药物发现

背景情况:

增强器Zeste同源2 (EZH2) 是一个关键的表观遗传调节器,涉及到各种癌症.
开发强效和选择性的EZH2抑制剂对于向癌症治疗至关重要.
现有的抑制剂在有效性和安全性方面面临挑战.

研究的目的:

通过生成深度学习和计算药物设计的结合,识别新型EZH2抑制剂.
评估已识别的化合物的结合亲和力,药理动力学特性和结构稳定性.
为加速新抗癌疗法的发现提供数据驱动的方法.

主要方法:

精心调整的REINVENT生成模型用于*de novo*分子设计.
基于结构和带的计算方法,包括分子对接和MM/GBSA.
定量结构-活动关系 (QSAR) 建模和密度函数理论 (DFT) 计算.
药物动力学 (PK) 和分子动力学 (MD) 模拟.

主要成果:

生成的分子表现出类似药物的特性和强大的分类性能 (ROC-AUC ≥ 0.87).
与Tazemetostat相比,四种化合物 (161,225,234,383) 显示出优越的结合亲和力.
化合物234和225显示改善了口服吸收,透性和降低了心脏毒性.
化合物383在分子动力学模拟过程中表现出增强的结构稳定性.

结论:

生成式建模和计算方法的综合方法有效地确定了有前途的EZH2抑制剂.
化合物234,225和383代表了进一步临床前开发的先进支架.
这项研究表明了加速发现新型表观遗传调节器的强大策略.

关键词:

在 DFT 方面,它是最重要的.在 EZH2 中使用.生成性AI是一种人工智能.分子动力学分子动力学虚拟选是一个虚拟的选.

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