基于ML的模型作为发现针对受体通道的新型抗药物的战略
Priyanka Andola1, Mukesh Doble2
1University of Hyderabad, Hyderabad-500046, India.
Current topics in medicinal chemistry
|October 23, 2024
概括
机器学习通过分析电压关闭通道抑制剂,准确地预测症候选药物. 一R,J48和Bagging模型在识别用于治疗的活性化合物方面表现出很高的性能.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 在生物信息学中的机器学习.
背景情况:
- 是一种常见的慢性神经系统疾病,需要长期治疗.
- 目前的治疗方法旨在控制症状,但需要新的治疗策略.
- 电压通道 (VGSCs) 因神经元刺激性增加而与有关.
研究的目的:
- 为了比较机器学习分类器来预测化合物对人类NaV1.7蛋白的抑制活性.
- 确定最有效的机器学习模型,用于将分子分类为针对这种目标的活性或非活性.
- 利用计算工具加速发现潜在的治疗方法.
主要方法:
- 利用Weka软件对来自ChEMBL的1781种化合物的数据集进行机器学习分析.
- 使用ChemDes服务器进行定量结构-活性关系 (QSAR) 分析的计算分子指纹.
- 通过使用精度,RMSE,ROC,MCC,精度,回忆和F-measure.measure等指标评估各种Weka分类器 (例如OneR,J48,Bagging).
主要成果:
- 一个R分类器在预测NaV1.7蛋白质的活性,非活性和中间化合物方面表现出卓越的性能.
- J48和Bagging分类器也表现出高准确度,MCC为1,ROC区域为1,RMSE接近零.
- 这些结果凸显了ML模型在对潜在药物候选物进行分类方面的有效性.
结论:
- 机器学习工具提供了一种快速,具有成本效益的方法来识别潜在的药物抑制剂.
- 一R,J48和Bagging模型有效地区分了针对人类NaV1.7蛋白的活性和非活性化合物类别.
- 这些预测模型可以在合理设计新型抗药物方面发挥重要作用.
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