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在疟疾中基因表达,对接和机器学习药物发现:系统性审查

Reuben Samson Dangana¹, Israel Ehizuelen Ebhohimen², Samson Anjikwi Malgwi¹

¹Discipline of Genetics, School of Life Sciences, University of KwaZulu-Natal (Westville), Durban, South Africa.

Biochemistry research international

|March 2, 2026

概括

分子对接和机器学习通过识别强大的草药化合物来加速疟疾药物发现. 整合多组学数据可以增强针对抗疟疾菌株耐药性的新型抗疟疾疗法的标识.

科学领域:

* 药理学药理学药理学
* 计算生物学 * 计算生物学
* 医学化学医学化学

背景情况:

* 疟疾是全球卫生面临的重大挑战,原因是药物耐药性增加.
*对于疟疾药物发现,迫切需要新的治疗策略.
* 本综述重点关注用于抗疟疾药物开发的分子方法和草药.

研究的目的:

*从2014年到2024年,系统地审查疟疾药物发现的分子方法.
* 确定新的药物标和化合物,特别是草药.
* 探索计算方法的整合,以了解菌生物学的方法.

主要方法:

* 按照PRISMA指南进行系统的文献搜索 (PubMed,Scopus,科学网).
* 提取基因表达,分子对接,机器学习模型和实验验证的数据.
*对64项选定的研究进行分析.

主要成果:

*分子对接是最常见的技术 (32.37%),其次是体外测试 (14.39%).
*RNA-seq确定了由草药治疗 (例如,亡,炎症) 调节的宿主/寄生虫基因.
*有前途的化合物 (isorhamnetin,myricetin 3-O-glucoside) 显示出与药物标 (Plasmepsin II,PfLDH) 的高结合亲和力.
*机器学习模型实现了高预测准确度 (AUC高达0.87) 的生物活性.
*主要的向途径包括血红蛋白降解和糖解.

结论:

* 整合多态,对接和机器学习可以提高目标识别和复合优先级.
* 分子技术显示出开发非耐药抗疟药物的巨大潜力.
*体内数据的差距和方法上的不一致性阻碍了临床翻译.
*未来的研究应该集中在标准化协议和研究协同作用的植物化学组合上.

关键词:

发现了抗疟疾药物的发现.种类的等离子杆菌物种.基因表达分析基因表达分析机器学习是机器学习.分子对接的分子对接.

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