基于DFT-ML的过渡金属复合物光敏化剂的DFT-ML属性预测用于光动力学治疗
Jingxing Gao1, Yachao Dong1, Tian Qiu1
1School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
ACS omega
|November 17, 2025
概括
这项研究引入了一种混合量子化学和机器学习模型,用于预测过渡金属复合物光敏化剂的性能,用于光动力学治疗. 该框架准确地选新型光敏感剂,加速癌症治疗研究.
科学领域:
- 光动力学疗法 光动力学疗法
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 光动力疗法 (PDT) 利用光敏剂和光用于癌症治疗,对过渡金属复合物 (TMC) 的兴趣日益增加.
- 合成和测试光敏剂是资源密集型,需要预测选方法.
- 现有的定量结构-属性关系 (QSPR) 模型往往缺乏TMC光敏剂的特异性.
研究的目的:
- 开发和验证一种混合机械和数据驱动的QSPR模型,用于预测六坐标TMC光敏剂的单片氧量子产量.
- 整合量子化学描述器与机器学习,以实现高效的合成前选.
- 为TMC光敏剂研究建立一个综合的DFT-ML框架,解决小型数据集的挑战.
主要方法:
- 密度函数理论 (DFT) 计算以获得激发状态量子化学描述符 (例如,激发能量).
- 单一和混合机器学习 (ML) 模型的开发,包括支持向量回归,内核回归,三角学习和专家混合.
- 使用DFT和分子性质的描述符来训练ML模型来预测单个氧量子产量.
- SHAP分析用于解释模型预测并验证描述符的相关性.
主要成果:
- 单个ML模型 (SVR,KRR) 实现了高预测准确性 (测试组R2>0.9,外部测试组R2>0.7).
- 混合模型 (delta-learning,MoE) 证明了改进的概括性 (在外部测试集上R2高达0.87) 和普遍性.
- SHAP分析证实了所选量子化学描述符的机理相关性.
- 该研究介绍了首个针对TMC光敏剂研究而定制的综合DFT-ML框架,数据有限.
结论:
- 开发的混合DFT-ML框架为TMC光敏剂性能提供了准确可靠的预测.
- 这种方法显著提高了发现新型光敏剂用于光动力学治疗的效率.
- 该框架为在具有小实验数据集的领域 (如TMC光敏剂研究) 的QSPR建模提供了强大的解决方案.
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