注意肌肤:通过可解释的机器学习方法预测化学品的皮肤腐蚀/刺激潜力
Zejun Huang1, Shang Lou1, Haoqiang Wang1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Chemical research in toxicology
|January 31, 2024
概括
这项研究开发了改进的in silico模型来预测皮肤腐蚀和刺激,为动物试验提供了可靠的替代方案. 新模型利用全面的数据集和机器学习来加强化学品安全评估.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 化学品安全评估 化学品安全评估
背景情况:
- 皮肤腐蚀/刺激 (Corr./Irrit.) 的情况 构成重大危险,对动物试验的限制越来越大,推动了对可靠的in silico替代品的需求.
- 现有的计算模型用于预测皮肤腐蚀/刺激. 通常受到数据可用性,质量和可访问性的限制.
研究的目的:
- 开发准确和可解释的in silico模型来预测皮肤腐蚀和刺激.
- 为了创建一个高质量的,可追溯的共识数据集皮肤Corr./Irrit. 预测. 预测. 预测. 这就是预测.
- 为化学安全评估提供可访问的工具.
主要方法:
- 编制了来自多个数据库的731个腐蚀,1283个刺激和1205个负样本的共识数据集.
- 开发了使用五个机器学习算法和六个分子表示的二进制分类器.
- 采用SHapley添加式解释 (SHAP) 来实现模型解释性和识别结构性警报.
主要成果:
- 在10倍交叉验证中实现了高性能:97.1%的Corr.的AUC. 负数与负数对比 和84.7%的刺激AUC. 负数与负数对比
- 注意的FP分类器在对Corr.的外部验证上,与现有的in silico工具相比,表现优越. 负数与负数对比
- 确定了关键的分子特征和结构警报,有助于皮肤腐蚀/刺激.
结论:
- 开发的共识数据集和可解释的机器学习模型在Silico预测Skin Corr./Irrit.方面取得了显著的进步.
- 专注的FP模型为动物试验提供了一个强大的替代方案,促进有效的化学安全评估.
- 通过AttentiveSkin和admetSAR平台提供集成模型,促进更广泛的可访问性和应用.
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