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分子对接辅助机器学习,用于识别针对细胞癌的潜在VEGFR抑制剂

Vidya Sagar Jerra¹, Balajee Ramachandran², Shaik Shareef¹

¹Department of Chemistry, School of Applied Sciences & Humanities, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India.

Medical oncology (Northwood, London, England)

|July 9, 2024

概括

这项研究重新定位了FDA批准的药物,以向血管内皮生长因子受体2 (VEGFR-2) 用于细胞癌治疗. 包括帕佐帕尼布在内的几种化合物显示出显著的结合亲和力和稳定性,表明潜在的抗癌疗效.

科学领域:

在瘤学瘤学.
药理学药理学是指药理学的学科.
计算化学计算化学

背景情况:

细胞癌 (RCC) 是一种与血管内皮生长因子 (VEGF) 表达相关的血管瘤.
VEGF-2及其受体是抗癌疗法的关键标,特别是用于抑制血管生成.

研究的目的:

选FDA批准的药物用于对抗VEGF受体2 (VEGFR-2) 作为潜在的抗癌策略.
通过计算方法识别VEGFR-2的新型抑制剂.

主要方法:

使用分子对接,分子动力学,机器学习和密度函数理论 (DFT) 对150种FDA批准的药物的选.
评估候选药物的结合能量,分子稳定性和物理化学性质 (logP).
使用K=10集群用于机器学习分析.

主要成果:

帕佐帕尼布,阿托基潘特,德罗斯佩里诺,雷维芬素和赞努布鲁丁尼布对VEGFR-2的结合能量为-7.0到-9.5千卡路里/mol.
分子动力学模拟证实了这些化合物的稳定性超过500ns.
在MM/GBSA分析中,可约束的自由能量值在-44.816和-82.582 kcal/mol之间.
机器学习识别了具有高结合能和有利的logP值的集群.

结论:

已识别的化合物显示出具有抑制VEGFR-2的显著潜力.
这项研究为对抗细胞癌的这些候选药物的实验验证提供了一个计算基准.

关键词:

药物重新定位是药物重新定位.机器学习机器学习分子对接是分子对接.分子动力学模拟的模拟.细胞癌是细胞癌.在VEGFR-2的基础上,

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