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作为TLR4/MD2抑制剂的T19093的计算识别和抗炎评估

Kuida Chen¹, Ke Shi², Tong Jin¹

¹State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing 210023, China.

Current topics in medicinal chemistry

|February 20, 2025

概括

机器学习确定了T19093,一种来自植物的化合物,作为一种新型的Toll-like受体4 (TLR4) / myeloid分化蛋白-2 (MD2) 抑制剂. 通过破坏TLR4/MD2相互作用和下游信号传递,T19093显示出强大的抗炎作用.

科学领域:

药用化学医学化学
计算机化药物发现技术
药理学药理学是指药理学的学科.

背景情况:

收费类受体4 (TLR4) /骨髓分化蛋白-2 (MD2) 复合体是抗炎药物开发的关键目标.
现有的TLR4/MD2抑制剂具有局限性,包括不良影响,导致需要更安全的替代品.

研究的目的:

确定具有抗炎症潜力的新型TLR4/MD2抑制剂.
为药物发现利用机器学习和虚拟查.

主要方法:

机器学习模型开发使用MACCS键指纹.
虚拟选和分子对接以评估与TLR4/MD2.2的结合亲和力.
用ADMET预测可药物性质和模拟分子动力学来确定结合稳定性.
使用LPS治疗的THP-1细胞进行体外验证,以评估抗炎疗效.

主要成果:

来自Gnaphalium属的多化合物T19093对TLR4/MD2表现出强烈的结合 (-11.29 kcal/mol).
通过ADMET预测,T19093显示出有利的药理动力学和物理化学特性.
分子动力学证实了稳定的T19093-TLR4/MD2复合体结合.
通过抑制TLR4/MD2信号传递,T19093有效降低了LPS诱导的炎症反应.

结论:

T19093是一种有前途的新型TLR4/MD2抑制剂,通过计算方法确定.
T19093具有显著的抗炎活性,为与炎症相关的疾病提供了潜在的治疗策略.

关键词:

抗炎药物抗炎药物抗炎药物抑制剂的抑制剂机器学习是机器学习.分子对接. 分子对接.在TLR4/MD2中.虚拟选是一个虚拟的选.

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