对内分泌干扰化学物质的结构特征进行建模和洞察
Ruiqiu Zhang1, Bailun Wang2, Ling Li1
1Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
Ecotoxicology and environmental safety
|July 14, 2023
概括
人工智能模型被开发用于通过准关键受体来识别内分泌干扰化学物质 (EDC). 这些模型,以及已识别的结构性警报,有助于EDC歧视和对药物发现和环境安全的机制理解.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 分子生物学分子生物学
背景情况:
- 内分泌干扰化学物质 (EDC) 对人类健康和生态系统构成重大风险.
- 准确和快速识别EDC对于风险评估和缓解至关重要.
- 现有的EDC预测计算模型往往缺乏可访问性和详细的结构分析.
研究的目的:
- 开发先进的人工智能 (AI) 模型来预测EDC活动.
- 研究EDC的结构特征和物理化学特性.
- 为EDC识别和机制理解创建一个用户友好的工具.
主要方法:
- 开发针对雄激素受体 (AR),雌激素受体 (ER) 和孕前X受体 (PXR) 的AI模型.
- 使用平衡准确度指标验证模型性能,用于绑定测试.
- 对物理化学性质的分析,以确定EDC与非EDC之间的区别特征.
- 识别表明EDC活动的结构性警报.
主要成果:
- 人工智能模型的预测准确度很高:AR为87.37%,ER为90.13%,PXR为79.21%.
- 在EDC和非EDC之间观察到物理化学性质的显著差异 (p < 0.05).
- 用于EDC识别的结构性警报已成功集成到SApredictor网络服务器中.
结论:
- 开发的AI模型为预测EDC受体结合提供了有效的工具.
- 物理化学性质分析和结构警报增强对EDC机制的理解.
- SApredictor网络服务器为EDC在药物发现和环境风险评估中的歧视提供了有价值的资源.
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