开发和实验验证3D QSAR模型用于查甲状腺过氧化酶抑制剂,使用计算化学的综合方法进行实验验证
Bharath Basavapattana Rudresh1, Abhishek Kumar Tater1, Vaibav Barot1
1Jai Research Foundation, Valvada, Vapi, Gujarat, 396105, India.
这项研究开发了一种计算模型,以识别抑制甲状腺过氧化酶 (TPO) 的甲状腺破坏性化学物质 (TDC). 该模型准确地预测了TPO抑制和选择性,为潜在的内分泌干扰者提供了更快的选方法.
科学领域:
- 内分泌学和毒理学.
- 计算化学和化学信息学
背景情况:
- 内分泌干扰剂 (EDs) 干扰激素系统;甲状腺干扰剂 (TDs) 特别影响甲状腺轴.
- 目前的甲状腺功能测试可能检测到不良影响,但不能检测到作用机制.
- 甲状腺过氧化酶 (TPO) 和化同载体 (NIS) 是甲状腺干扰的关键目标.
研究的目的:
- 开发和验证用于识别选择性甲状腺过氧化酶抑制剂的计算模型.
- 利用3D-QSAR和机器学习快速选潜在的甲状腺破坏性化学物质.
- 评估已识别的抑制剂对人类NIS的选择性.
主要方法:
- 同质模型被用来创建人类TPO (hTPO) 和人类NIS (hNIS) 的3D结构.
- 分子对接和3D-QSAR被用来分析190个已知的hTPO抑制剂.
- 使用实验数据训练和验证了kNN和随机森林机器学习模型.
主要成果:
- 计算模型在从外部数据集中定性识别TPO抑制剂时实现了100%的准确性.
- 对接研究证实了对测试分子的TPO对NIS的选择性抑制.
- 开发的模型为TDCs的早期查提供了一种可靠的方法.
结论:
- 计算建模,包括3D-QSAR和机器学习,对于识别选择性甲状腺过氧化酶抑制剂是有效的.
- 这种方法可以通过早期预测内分泌干扰潜力来加速发现更安全的化学物质.
- 该研究强调了在毒理学评估中了解分子机制的重要性.
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