EosCloud的SbD4Skin:整合多视图分子表示来预测皮肤敏感性,刺激性和急性皮肤毒性
Nikoletta-Maria Koutroumpa1,2, Dimitra-Danai Varsou3, Panagiotis D Kolokathis3
1Entelos Institute, Nicosia 2102, Cyprus.
Computational and structural biotechnology journal
|August 20, 2025
概括
这项研究使用多种分子表示和机器学习来预测皮肤毒性,减少动物测试的计算框架. 开发的模型可在SbD4Skin平台上进行化学风险评估.
科学领域:
- 计算毒理学
- 在的方法
- 化学风险评估
背景情况:
- 伦理,财务和科学挑战需要非动物测试方法来评估化学毒性.
- 预测皮肤敏感性,刺激/腐蚀和急性皮肤毒性对于人类健康风险评估至关重要.
- 现有的方法往往依赖于动物试验,因此需要采用替代的式方法.
研究的目的:
- 开发和验证用于预测关键皮肤毒性终点的计算框架.
- 评估各种分子表示 (MACCS键,摩根指纹,Mordred描述符) 在毒性预测中的有效性.
- 提高模型的透明度,并提供对化学毒性的机制性见解.
主要方法:
- 使用机器学习算法 (随机森林,SVM,k-NN) 来评估单个分子表示.
- 开发了一个多视图完全连接的神经网络 (FCNN),集成了各种分子描述器.
- 使用Shapley添加式解释 (SHAP) 进行特征重要性分析和模型解释性.
主要成果:
- 多视图FCNN模型表现出优于或与单个表示模型相比的预测性能.
- 达到高AUC值:刺激/腐蚀为0.91,敏感性为0.88,急性皮肤毒性为0.82.
- 在试验组中成功识别了86%的皮肤敏感剂,89%的刺激物和86%的皮肤毒性化合物.
结论:
- 该计算框架有效预测皮肤毒性终点,为动物试验提供了可行的替代方案.
- 通过SbD4Skin网络平台,可以免费访问验证的模型,以支持监管决策.
- 公平的数据集和模型促进化学风险评估的透明度,可重复使用性和接受性.
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