人工智能驱动的光探测器发现加速
Xuefeng Jiang1, Yanbo Li1, Xue Tian1
1National Clinical Research Center for Children and Adolescents' Health and Diseases, Children's Hospital of Chongqing Medical University, Chongqing, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|December 24, 2025
概括
这项研究介绍了PROBY,一种人工智能模型,通过预测分子性质来加速光成像探测器的发现. 这种人工智能生物测试方法有效地识别出用于临床和研究应用的新型探针.
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 医疗成像医学成像
背景情况:
- 光成像探针对于临床和临床前研究至关重要,但其发现受到有限的支架和缓慢,昂贵的试错方法的阻碍.
- 开发针对特定目标的探测器需要克服在识别合适的化物支架和预测光物理性质方面的挑战.
研究的目的:
- 开发一种混合战略,将人工智能 (AI) 与生物测试相结合,以加速发现特定目标的光成像探头.
- 创建一个能够识别光分子并预测关键光物理性质的AI模型 (PROBY).
主要方法:
- 开发了PROBY,一个人工智能模型训练了来自九个数据集的100多万个分子条目,以识别光分子并预测七个光物理性质.
- 将PROBY应用于26,416个目标验证分子的库中,以识别具有目标亲和力和有利光学特性的候选分子.
- 通过化学修饰验证了AI识别的临床相关标候选物 (tau,BCL-2,TDP-43) 并优化了一种化合物 (PE859).
主要成果:
- PROBY确定了数千个具有目标亲和力和可取光学特性的候选光探针.
- 发现了PE859,obatoclax和B3,这些应用得到了验证,包括光谱分析,药物查,病理标记和体内成像.
- 优化衍生品859-2启用了在体内对转基因小鼠tau病理的两光子成像.
结论:
- 混合人工智能-生物测试策略显著扩大了设计针对特定目标的光探测器的可访问的支架景观.
- 这种方法为下一代光探测器发现提供了一个可扩展,高效和具有成本效益的框架.
- 开发的AI模型和验证的探测器为推进临床导航和临床前研究提供了强大的工具.
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