神经TDPi:可解释的深度学习模型与多模式融合,用于识别神经毒性化合物
Baodi Liu1, Zhaoyang Chen2,3, Nianlu Li1
1Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Neurosurgery, Jinan, 250014, Shandong, China.
Journal of chemical information and modeling
|January 29, 2026
概括
一个新的深度学习模型NeuroTDPi通过整合分子数据准确预测化学神经毒性. 该工具有助于早期药物开发和环境风险评估,识别潜在的神经毒性化合物及其结构风险.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 在药物发现中使用机器学习.
背景情况:
- 化学神经毒性是药物开发和环境安全的一个重要问题.
- 早期预测神经毒性可以降低实验成本并加速安全评估.
- 目前的方法可能缺乏大规模查所需的准确性和效率.
研究的目的:
- 开发和验证NeuroTDPi,一个用于预测化学神经毒性的深度神经网络模型.
- 整合多模式数据,在多个神经毒性终点上提高预测准确度.
- 提高神经毒性预测的解释性,并识别结构性警报.
主要方法:
- 开发了NeuroTDPi,一个多层完全连接的深度神经网络.
- 采用了多式联络融合策略,整合了分子表征和终点特定特征.
- 利用夏普利添加物解释 (SHAP) 进行模型解释和确定关键化学性质.
- 使用接收器运行特征曲线 (AUC) 下面的面积来评估模型性能.
主要成果:
- 神经TDPi实现了高预测性能,AUC值为0.97 (血脑屏障透性),0.84 (神经毒性) 和0.82 (哺乳动物神经毒性).
- SHAP分析提供了对影响神经毒性预测的物理和化学性质的见解.
- 确定了与神经毒性化合物相关的特定结构警报,提供了机械学理解.
结论:
- NeuroTDPi为早期神经毒性评估提供了一个强大的和可解释的平台.
- 该模型通过提供可操作的化学安全结构见解来促进风险评估.
- 在https://www.sapredictor.cn/的免费资源支持在毒理学和药物开发中的更广泛应用.
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