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基于深度学习算法和分子指纹的药物诱导毒性预测

Shuailong Wang¹, Yan Li²

¹State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, 116024, Liaoning, China.

Molecular diversity

|October 12, 2025

概括

这项研究开发了计算模型来预测药物诱导毒性 (DIN). 具有分子指纹的深度神经网络显示出对潜在药物候选者的早期查有前途,识别损伤的关键结构警报.

科学领域:

计算毒理学和化学信息学
药理学和药物安全性评估.
生物医学数据科学是生物医学数据科学.

背景情况:

药物诱导性毒性 (DIN) 是一种复杂的药物不良反应,具有具有挑战性的临床前预测.
在形方法为早期DIN风险评估提供了一个有希望的替代方案.
一个全面的数据集对于开发可靠的预测模型至关重要.

研究的目的:

建立与药物诱导毒性相关的化合物的高质量数据集.
构建和比较各种分类模型来预测DIN.
使用可解释的AI识别导致毒性的关键结构特征.

主要方法:

从权威来源 (SIDER,FDA,ChEMBL,DrugBank,文献) 系统地收集和注释了1018种化合物.
开发了42个使用深度神经网络 (DNN) 的分类模型和6个具有6个分子指纹的机器学习算法.
应用夏普利添加式解释 (SHAP) 模型可解释性和影响性子结构的识别.

主要成果:

在所有分子指纹上,DNN模型的性能始终优于传统的机器学习算法.
使用DNN的ECFP_6指纹获得了最高的性能:AUC为75.9%,ACC为71.4%,F1得分为76.0%.
SHAP分析确定了十个关键结构碎片作为DIN的潜在早期预警标记.

结论:

使用分子指纹的深度神经网络模型是预测药物诱导毒性的有效工具.
这些模型可以帮助在药物开发过程中早期评估潜在候选药物的风险.
识别的结构碎片为未来的毒性查和药物设计提供了宝贵的见解.

关键词:

深度神经网络是一个神经网络.药物诱导的毒性药物诱导的毒性机器学习机器学习分子指纹是分子指纹.沙普利的添加剂解释解释结构性警报结构性警报

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