一种用于预测分子抗微生物活性的机器学习方法
Bangjiang Lin1,2, Shujie Yan3,4, Bowen Zhen3,4
1Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Quanzhou, 362216, China. linbangjiang@fjirsm.ac.cn.
Scientific reports
|February 24, 2025
概括
我们开发了MFAGCN,这是一种机器学习模型,使用分子指纹和图形表示来预测抗菌疗效. 该方法有助于发现新型抗生素,并确定药物开发的关键功能组.
科学领域:
- 计算化学计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 抗生素耐药性是一个日益增长的全球健康威胁.
- 传统的抗生素发现方法面临着局限性.
- 需要新的计算方法来加速药物发现.
研究的目的:
- 引入MFAGCN,一种用于预测抗微生物药物有效性的机器学习模型.
- 为了利用分子指纹 (MACCS,PubChem,ECFP) 和图形表示来进行增强的预测.
- 确定在抗微生物活性中具有影响力的分子功能组.
主要方法:
- 开发了MFAGCN,一个带有注意力机制的图形卷积网络模型.
- 集成多个分子指纹 (MACCS,PubChem,ECFP) 和分子图的功能.
- 在公共数据集上对基线模型进行比较实验.
- 进行功能组分布分析和与已知的抗生素进行结构相似性分析.
主要成果:
- 与两个公共数据集的基线模型相比,MFAGCN表现优越.
- 分析验证了模型的预测,并强调了功能组的重要性.
- 结构相似性分析成功地阻止了现有抗生素的重新发现.
结论:
- MFAGCN提供了一种快速有效的查具有抗菌潜力的分子方法.
- 该模型为驱动抗微生物活性的功能组提供了有价值的见解.
- 这种方法加速了新型抗生素的开发,以对抗耐药性.
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