药物分子表示用于药物反应预测:通过机器学习方法进行全面的调查
Meisheng Xiao1, Qianhui Zheng1, Paul Popa2
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Scientific reports
|January 3, 2025
概括
将PubChem指纹或SMILES等药物分子表示与深度学习模型相结合,可以显著改善药物反应预测 (DRP). 最好的代表性取决于具体的模型和任务,帮助药物重新定位和个性化医疗.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物分子表示是药物反应预测 (DRP) 预测模型中的标准.
- 结合DRP分子表征与遗传特征的比较有效性尚未得到充分理解.
- 准确的DRP模型对于药物重新定位,个性化药物和新药发现至关重要.
研究的目的:
- 在DRP模型中全面评估各种药物分子表示的疗效.
- 评估将分子表征与DRP的遗传特征结合在一起的影响.
- 为了确定最佳的药物分子表示,以提高DRP模型的性能.
主要方法:
- 用于DRP的尖端机器学习模型.
- 评估了各种药物分子表示,包括PubChem指纹和SMILES.
- 在不同的环境下进行实验,以比较模型性能.
主要成果:
- 包括PubChem指纹或SMILES在内显著提高了深度学习模型的DRP性能.
- 最佳的药物分子表示取决于预测模型和特定的DRP任务.
- 证明了分子表示在提高DRP精度方面的价值.
结论:
- 药物分子表示,特别是PubChem指纹和SMILES,对于改善DRP模型至关重要.
- 选择合适的分子表示是建立高效的DRP预测模型的关键.
- 这些发现为优化药物重定向,个性化药物和药物发现的DRP模型提供了指导.
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