使用机器学习模型预测化学化合物的抗SARS-CoV-2活动
Beihong Ji1, Yuhui Wu1, Elena N Thomas1
1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Artificial intelligence chemistry
|December 13, 2023
概括
机器学习模型准确地预测了COVID-19药物发现的抗SARS-CoV-2活性. 使用GAFF+RDKit描述符的k-最近邻居 (KNN) 模型实现了最佳性能,优于其他算法和模型.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的作用
- 传染病治疗方法 传染病治疗方法
背景情况:
- 加快发现新型冠状病毒疾病2019 (COVID-19) 治疗方法至关重要.
- 预测抗SARS-CoV-2活动的现有方法需要优化.
- 机器学习 (ML) 提供了一种有前途的方法来增强药物发现管道.
研究的目的:
- 开发和评估ML模型来预测查化合物的抗SARS-CoV-2活动.
- 确定最佳的ML算法和分子描述器,以准确预测活动.
- 创建一个用户友好的Web服务器来预测任意化合物的抗SARS-CoV-2活性.
主要方法:
- 探索了6个ML算法 (包括k-近邻 - KNN) 与15个分子描述符.
- 在COVID-19开放数据门户中利用了9个查试验的数据.
- 开发了基于描述器 (COVID-19-CP) 和基于图形 (注意力FP) 的模型.
- 将模型性能与REDIAL-2020进行比较,并开发了一个共识预测模型.
主要成果:
- 使用GAFF+RDKit描述符的KNN模型显示出最佳性能 (平均精度为0.68,AUC为0.74).
- 基于描述器的KNN模型优于其他ML算法和描述器.
- 注意的FP模型显示了与COVID-19-CP相似的性能,并且表现优于REDIAL-2020.
- 与单个模型相比,共识预测显著提高了准确性.
结论:
- 开发了准确的ML模型 (COVID-19-CP和注意力FP) 来预测抗SARS-CoV-2活动.
- 使用GAFF+RDKit描述符的KNN模型对COVID-19药物发现非常有效.
- 有一个Web服务器可用于预测化合物活性,帮助治疗开发.
- 共识预测策略可以提高识别COVID-19候选药物的成功率.
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