使用化学信息和转录学数据预测药物诱导的毒性
Hemanth Chenga1, Ayush Garg2, Shyam Sundar Das3
1TCS Research (Life Sciences Division), Tata Consultancy Services Limited, Chennai 600113, India.
Journal of chemical information and modeling
|May 9, 2025
概括
预测药物诱导的脏毒性至关重要. 用机器学习分析的基因表达数据提供了机械的洞察力和更高的预测准确性,优于传统的化学模型.
科学领域:
- 计算毒理学计算毒理学
- 药物基因组学 药物基因组学
- 机器学习在药物发现中的作用
背景情况:
- 药物诱导性毒性预测在药物开发中至关重要.
- 基因表达数据提供了对器官毒性的机制性见解.
- 当前的计算模型通常依赖于化学信息.
研究的目的:
- 用机器学习评估基因表达数据,用于预测毒性.
- 将基因表达基因模型的性能与基于化学信息的模型进行比较.
- 确定与药物诱导毒性相关的关键基因.
主要方法:
- 使用LightGBM,随机森林,支持矢量机和XGBoost进行预测.
- 探索了基因表达数据的子集 (6000,9000,12000个配置文件).
- 应用了诸如最佳概率值,数据平衡和成本敏感学习等技术.
主要成果:
- 基于基因表达的模型实现了高性能,最佳模型 (GEM20) 达到0.94.9的AUC.
- 最顶级的基于化学信息的模型 (CIM19) 和初始基因表达模型 (GEM9) 显示了类似的AUC (0.89-0.9).
- 在SHAP分析中,发现了潜在的毒性相关基因:CDC20,RPS6,DDIT4,GAPDH,CCNF和MRPL12.
结论:
- 基因表达数据,特别是当最佳选择和平衡时,显著提高了毒性预测的准确性.
- 使用基因表达特征的机器学习模型为了解和预测药物诱导的损伤提供了强大的工具.
- 鉴定出来的基因为缓解药物诱导的毒性提供了潜在的生物标志物和机制性标.
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