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通过机器学习建模,分子对接,MD模拟和DFT研究,发现了针对甲状腺癌的强有力的ALK氨酸激酶抑制剂

Muhammad Naveed Anjum¹, Sumaira Nadeem², Md Al-Amin³

¹Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan 60800, Pakistan.

Computational biology and chemistry

|February 20, 2026

概括

这项研究使用了包括机器学习和分子对接在内的计算方法,以发现用于甲状腺癌 (TC) 药物发现的新型无细胞淋巴瘤激酶 (ALK) 抑制剂. 确定了五种强效化合物,显示出开发新型抗TC疗法的前景.

科学领域:

计算化学和化学信息学
药物发现和药物化学
瘤学和癌症治疗学

背景情况:

甲状腺癌 (TC) 需要先进的药物发现,以进行有效的治疗管理.
探索新型无细胞淋巴瘤激酶 (ALK) 氨酸激酶抑制剂对于开发新疗法至关重要.
当前的治疗策略需要创新的方法来克服先进药物发现的局限性.

研究的目的:

确定用于甲状腺癌 (TC) 治疗的新型无细胞淋巴瘤激酶 (ALK) 铁氨酸激酶抑制剂.
采用集成机器学习 (ML),QSAR,分子对接和网络药理学的计算管道.
通过先进的计算技术加速甲状腺癌药物发现过程.

主要方法:

一个结合ML算法,QSAR建模,分子对接,分子动力学 (MD),密度函数理论 (DFT) 和网络药理学的计算管道.
来自CHEMBL4247数据库的3546种化合物的选,使用Lipinski的五项规则和PADEL描述符进行过.
利用集体ML模型 (投票分类器) 进行高精度的复合物优先排序,并通过MD模拟和MM/PBSA分析验证顶级命中.

主要成果:

一个整体ML模型实现了高预测准确性 (ROC-AUC = 0.99),优先考虑活性化合物.
分子对接确定了五个具有强大的结合亲和度 (-9.0到-10.4千卡/mol) 的顶部碰撞配体,超过了原生配体.
网络药理学揭示了参与TC的关键标 (AKT1,TP53) 和途径 (PI3K-Akt,MAPK),表明细胞生存和增殖的调节.

结论:

综合计算方法成功地确定了用于甲状腺癌药物发现的强有力的ALK抑制剂.
已识别的受影响化合物显示出对TC进展至关重要的瘤信号网络的调节潜力.
这项研究强调了结合ML和计算化学在加速甲状腺癌治疗开发中的有效性.

关键词:

亚纳普拉斯性淋巴瘤激酶.在 DFT 方面,它是最重要的.在MM-PBSASA中使用.机器学习算法机器学习算法甲状腺癌是什么?甲状腺癌是什么?

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