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ProtFPreDTI:基于ProtBERT深度语言模型与自适应模糊采样策略的药物向相互作用预测研究和LIME解释性分析

Yun Zuo¹, Xun Gu¹, Chen Zhang¹

¹School of Artificial Intelligence and Computer Science, Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, Wuxi 214122, China.

Bioconjugate chemistry

|January 13, 2026

概括

这项研究介绍了ProtFPreDTI,一种机器学习模型,通过集成先进的特征提取和组合方法,准确预测药物向相互作用. 它克服了传统方法的局限性,为药物发现提供了更快,更具成本效益的解决方案.

科学领域:

计算化学和化学信息学
生物信息学和计算生物学
机器学习在药物发现中的作用

背景情况:

药物向相互作用 (DTI) 分析对于药物研发至关重要.
传统的DTI分析实验方法耗时且昂贵.
现有的计算模型面临特征表征和数据不平衡的挑战.

研究的目的:

开发一种基于机器学习的新型药物向相互作用预测方法.
为了解决特征提取,数据不平衡和模型可解释性在DTI预测中的局限性.
提高药物发现过程的效率和准确性.

主要方法:

使用Mol2Vec用于药物分子特征提取和ProtBERT用于蛋白质序列特征提取.
采用SHAP价值分析用于定量特征重要性选,保留300个维度.
实施了基于模糊逻辑的低采样策略,用于数据平衡和XGBoost和随机森林的自适应加权融合,用于预测.
集成的LIME用于模型解释性.

主要成果:

在独立验证中,ProtFPreDTI模型实现了0.92的曲线下面积 (AUC).
与传统方法相比,在预测准确度,灵敏度和特异性方面取得了显著的改进.
该系统显示了增强的预测稳定性和跨数据集概括能力.

结论:

开发的ProtFPreDTI模型为预测药物向相互作用提供了强大而准确的解决方案.
该方法优化了从特征工程到结果解释的整个过程,提高了药物发现效率.
该方法为药物开发提供了科学和可追溯的决策基础.

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