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基于化学模拟的药物设计用于针对PI3K的癌症治疗:整合机器学习和分子建模

Mohammed A Bazuhair^1,2, Anwar A Alghamdi³, Othman Baothman⁴

¹Department of Clinical Pharmacology Faculty of Medicine King, Abdulaziz University, 21589, Jeddah, Saudi Arabia.

Molecular diversity

|August 17, 2024

概括

研究人员确定了一种新型化合物,885,387,作为一种潜在的PIK3CG抑制剂来对抗癌症. 计算方法选化合物,其中885,387种显示出优越的结合性和稳定性,用于进一步的实验研究.

科学领域:

生物化学生物化学
分子生物学分子生物学
计算化学计算化学

背景情况:

癌症的特征是不受控制的细胞生长和转移,由遗传和表观遗传改变驱动.
氨基酸3-激酶 (PI3K) 信号通路,特别是由PIK3CG编码的PI3Kγ,对于癌症的发展和进展至关重要.

研究的目的:

使用计算方法识别用于癌症治疗的新型PIK3CG抑制剂.
选化学碎片及其类似物与PIK3CG目标.

主要方法:

基于机器学习的结合估计和分子对接被用来选1015个化学碎片对PIK3CG.
模拟生成和随后的选确定了三种化合物,其中885,387种被选择用于进一步分析.
用分子动力学模拟和MM/GBSA计算来评估蛋白质-连接体复合物的稳定性和结合的自由能量.

主要成果:

化合物885,387在100 ns的模拟过程中显示出最稳定的偏差和一致的键形成.
与其他候选药物相比,MM/GBSA分析显示885,387种药物具有较高的结合自由能量 (ΔG = -18.80 kcal/mol).
计算分析确定了885,387作为一个非常有前途的候选抑制剂.

结论:

化合物885,387作为PIK3CG抑制剂具有显著的治疗潜力.
需要进一步的实验验证,以确认885,387在癌症治疗中的有效性.

关键词:

癌症癌症癌症癌症在MMGBSA中,我们可以看到MMGBSA.机器学习是机器学习.分子建模分子建模这就是PIK3CGCG.氏酸3-酶酶是一种酸.

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