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基于分子指纹的深度学习算法用于预测药物诱导的肝损伤

Qiong Yang¹, Shuwei Zhang¹, Yan Li¹

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

|February 2, 2024

概括

预测药物诱导性肝损伤 (DILI) 是至关重要的. 使用分子指纹的深度神经网络模型显示,在药物开发的早期确定DILI风险具有前途.

科学领域:

计算毒理学计算毒理学
药品化学药品化学是一个
药理学药理学是指药理学的学科.

背景情况:

药物诱导性肝损伤 (DILI) 是一种罕见但严重的药物不良反应 (ADR).
目前的临床前动物模型在预测DILI方面存在局限性.
在 silico 方法为 DILI 预测提供了一个有希望的替代方案.

研究的目的:

开发和评估用于预测DILI的计算模型.
确定与DILI风险相关的关键结构特征.
建立一个可靠的工具,用于早期肝毒性查.

主要方法:

组装了一个1573个化合物的数据集.
使用深度神经网络 (DNN) 和机器学习算法开发了48个分类模型.
使用分子指纹 (例如,ECFP_6) 和SHapley添加式解释 (SHAP) 来解释模型.

主要成果:

性能最好的模型是使用ECFP_6指纹的DNN,其AUC为0.713,BA为0.680和F1为0.753.
确定了导致DILI风险的关键结构碎片.
选择了前十个子结构作为肝毒性潜在警告指标.

结论:

基于分子指纹的深度神经网络模型对于DILI风险评估是有效的.
这些计算工具可以增强新药候选药物的安全查.
鉴定的结构碎片可以指导未来的药物设计和安全性评估.

关键词:

深度神经网络是一个神经网络.药物诱导的肝损伤是药物诱导的机器学习是机器学习.分子指纹是分子指纹.沙普利的添加剂解释解释

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