机器学习模型的开发,以识别潜在的抗结核活性化合物
Aman Rawat1, Saatvik Gupta1, Chiranjit Pal1,2
1University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi Campus, Patel Street, Vishwas Nagar Extension, Shahdara, Delhi, 110032, India.
Scientific reports
|December 27, 2025
概括
开发了机器学习模型,以识别新的结核病药物候选者. 这些模型分析了分子数据,以预测化合物活性,帮助紧急寻找对抗耐药结核病的治疗方法.
科学领域:
- 计算化学和生物信息学
- 药物的发现和开发和开发.
- 机器学习在药物化学中的应用.
背景情况:
- 结核病 (TB) 仍然是一个全球卫生危机,由多抗药 (MDR) 和广泛抗药 (XDR) 菌株加剧.
- 迫切需要新的抗结核病候选药物,需要有效的标识策略.
- 机器学习为加速药物发现管道提供了强大的工具.
研究的目的:
- 探索机器学习算法的应用,以识别潜在的抗结核化合物.
- 开发和评估用于预测结核病目标的化合物活性的分类模型.
- 用可解释的人工智能方法解释分子描述器对生物活性的贡献.
主要方法:
- 来自ChEMBL数据库的23791个分子的数据集被利用.
- 103个分类模型使用六个分子表示 (RDKitDes,MACCSFP,MorganFP,PairsFP,PubChemFP,RDKitFP) 构建.
- 采用了七个机器学习算法 (RF,XGBoost,DT,KNN,GNB,LR,ANN) 并使用十倍交叉验证和AUC指标进行评估,并使用SHAP进行解释.
主要成果:
- 多个机器学习模型展示了识别抗结核病化合物的预测能力.
- 该研究建立了一个框架,用于使用多种分子表示和算法快速识别.
- SHAP分析提供了对驱动结核病点的化合物活性的主要分子特征的见解.
结论:
- 机器学习方法有效地加速了针对结核病的新型候选药物的识别.
- 开发的模型和方法可以显著帮助打击耐药结核病.
- 整合分子表示,ML算法和可解释的AI提高了药物发现的效率.
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