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通过基于机器学习的虚拟查,分子对接和生物分子模拟研究来确定新型NLRP3抑制剂作为的治疗选择

Maryam Zulfat¹, Mohammed Ageeli Hakami², Ali Hazazi^3,4

¹Department of Biochemistry, Computational Medicinal Chemistry Laboratory, Abdul Wali Khan University, Mardan, Pakistan.

|August 22, 2024

概括

机器学习准确地确定了神经疾病的潜在NLRP3炎症酶抑制剂. 这项研究选了大型数据库,发现了治疗和炎症的有希望的化合物.

科学领域:

计算化学是一种计算化学.
药理学药理学是指药理学的学科.
机器学习在药物发现中的作用

背景情况:

NOD-Like受体蛋白-3 (NLRP3) 炎症体与等神经系统疾病有关.
针对NLRP3是一种有前途的治疗策略,用于控制中枢神经系统的炎症.

研究的目的:

开发和应用基于机器学习的虚拟查策略,用于识别NLRP3炎症酶的新型抑制剂.
预测蛋白质 - 连接体结合亲和力,减少药物发现中的错误阳性.

主要方法:

使用十倍交叉验证构建和验证了分类模型 (SVM,RF,KNN).
特性选择确定了关键的分子活性决定因素.
高性能模型选了大型化学数据库 (南非ZINC).
分子对接和动力学模拟 (MOE,Pyrx,珀工具) 评估了结合亲和力和稳定性.

主要成果:

随机森林模型实现了最高的准确性 (94.6%),灵敏度 (86%) 和特异性 (98%).
虚拟查发现了来自ZINC的98种化合物和来自南非数据库的39种化合物具有潜在的反NLRP3活性.
分子对接和动力学模拟证实了两个化合物的稳定结合 (ZINC000009601348和SANC00225).

结论:

基于机器学习的虚拟查对于识别NLRP3炎症酶抑制剂是有效的.
这些已识别的化合物显示出对NLRP3介导的神经疾病的治疗开发潜力.
进一步的化分析支持了化合物对NLRP3蛋白的抑制机制.

关键词:

是一种病.模拟MD的模拟方法机器学习是机器学习.分子对接是分子对接.在NLRP3中,NLRP3是NLRP3中的一个.

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