一个碎片意识模型用于新型精神活性物质分析,不确定性量化量化.
Pengfei Liu1, Jing Guo2, Jun Xie3
1School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, Guangdong Province, China.
Journal of pharmaceutical and biomedical analysis
|September 21, 2025
概括
一个新的AI模型,新型精神活性物质碎片意识化学语言模型 (NPS-FACL),改善了新兴合成药物的检测. 这种人工智能方法比法医毒理学的传统方法提供了更快的识别和更好的洞察力.
科学领域:
- 法医毒理学 法医毒理学
- 人工智能的人工智能
- 计算化学计算化学
背景情况:
- 新型精神活性物质 (NPS) 的扩散对法医毒理学构成了重大挑战.
- 传统的检测方法,如液态染色学-质谱学 (LC-MS),难以跟上NPS的快速出现和多样性.
- 需要先进的分析工具,能够快速识别和描述新兴合成药物.
研究的目的:
- 引入新型精神活性物质碎片意识化学语言模型 (NPS-FACL),这是一个旨在增强NPS检测的AI框架.
- 利用化学子结构来改善新型精神活性物质的识别和分析.
- 开发一种更主动和可解释的方法来识别新出现的合成毒品威胁.
主要方法:
- 开发化学语言模型的碎片意识代币化策略.
- 在AI框架内实施可解释的不确定性量化.
- 评估NPS-FACL模型在检测新型精神活性物质方面的表现.
主要成果:
- 碎片意识的代币化减少了20.33%的代币表示复杂性.
- 通过NPS-FACL模型,NPS检测F1得分增加了2.01%.
- 该模型提供了对基架驱动偏差的可解释的见解,提高了预测可靠性.
结论:
- NPS-FACL模型代表了人工智能辅助的NPS检测法医毒理学的重大进展.
- 这种方法提供了主动警报和不确定性驱动的见解,超越了传统LC-MS方法的局限性.
- 该框架对公共卫生监督,药物监管和减少危害策略有更广泛的影响.
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