使用机器学习方法对针对SARS-Cov-2的候选药物化合物的生物活性预测
Faisal Bin Ashraf1,2, Sanjida Akter3, Sumona Hoque Mumu4
1Department of Computer Science and Engineering, Brac University, Dhaka, Bangladesh.
PloS one
|September 5, 2023
概括
研究人员开发了一种机器学习模型来预测COVID-19药物对SARS-CoV-2 3CLpro的有效性,实现了高精度. 这种计算方法有助于在实验室测试前识别有力的抗病毒化合物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习在病毒学中的应用.
背景情况:
- SARS-CoV-2 3CLpro蛋白对于病毒复制至关重要,也是COVID-19的关键治疗标.
- 像ChEMBL和PubChem这样的现有数据库提供针对这个目标的小分子实验查数据.
- 开发预测模型可以加快有效抗病毒化合物的识别.
研究的目的:
- 设计和评估一种计算模型,用于预测药物化合物对SARS-CoV-2 3CLpro.
- 为了确定最佳的分子描述器来预测药物的生物活性.
- 使用大规模数据集验证预测模型.
主要方法:
- 使用各种分子描述符对27个机器学习分类器的评估.
- 开发一个神经网络模型来预测生物活性.
- 使用SHAP (SHapley添加式扩展) 和XGB分类器来识别PaDEL描述符中的重要分子指纹.
- 在超过10万个分子的数据集上对模型的验证.
主要成果:
- 神经网络模型在ChEMBL数据上实现了91%的准确性,在ChEMBL和PubChem数据的组合中达到93%.
- 不活性和活性化合物的F1分数分别为93%和94%.
- 帕德尔描述器在生物活性预测方面被证明是有效的,SHAP分析确定了关键的分子特征.
结论:
- 开发的神经网络模型对于预测SARS-CoV-2 3CLpro抑制剂活性是有效的.
- 该研究强调了分子描述符和机器学习在加速COVID-19药物发现方面的实用性.
- 需要进一步的体外和体外研究来确认已识别的潜在候选药物的疗效.
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