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对药物动力学特性和分子对接的in silico评估,用于识别潜在的抗癌化合物

Juan Pablo Betancourt Arango¹, Deisy Yuliana Montoya Rodriguez¹, Sebastián Lozano Cruz²

¹Universidad de Caldas, Chemistry Department, Chromatography and Related Techniques Research Group, Manizales, Caldas, Colombia.

Computational biology and chemistry

|August 13, 2025

概括

使用ADME(T) 分析和机器学习进行的计算药物发现发现,将CC-43化合物确定为乳腺癌治疗的有希望的候选物,因为它具有有利的特性和TLK2激酶抑制.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
药理动力学药理动力学

背景情况:

通过预测ADME-Tox参数,in silico方法可以加速药物开发.
QSAR,SAR和QSPR评估生物活性和药理动力学.

研究的目的:

通过计算来评估58种有机化合物的ADME (T) 概况.
开发毒性的预测模型.
评估TLK2激酶抑制乳腺癌和智力障碍的潜力.

主要方法:

使用MMFF94力场优化结构.
通过瑞士ADME和PreADMET计算的ADME-Tox描述器 (Log P,Log S,Caco-2,CYP450,hERG,LD50,DILI) 通过瑞士ADME和PreADMET进行了计算.
采用随机森林用于LD50预测和TLK2抑制的分子对接.

主要成果:

在Log P和Log D之间有很强的相关性;PCA揭示了结构分组.
随机森林模型准确地预测了LD50 (r2 = 0.8410).
化合物CC-43表现出强大的TLK2结合 (-8.2 kcal/mol) 与中等毒性 (LD50 = 3.186).

结论:

综合计算方法 (ADME,T,ML,对接) 提供了一个强大的药物发现策略.
鉴定出具有良好的药理动力学和TLK2抑制潜力的化合物.
化合物CC-43是乳腺癌治疗的有希望的候选者.

关键词:

ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME ADME 对交叉验证进行验证.分子对接分子对接随机的森林随机的森林

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