通过转移学习方法加速发现有前途的光热分子
Zhiwen Zhang1, Siwei Wu1, Liqiang He1
1School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, 100 Waihuanxi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Briefings in bioinformatics
|August 13, 2025
概括
这项研究引入了一种人工智能方法,可以快速找到新的,有效的和安全的酸光热剂,用于癌症治疗和生物成像. 该方法加速了这些关键分子的设计.
科学领域:
- 生物医学工程 生物医学工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 基于的分子由于其光学特性,对光热疗法具有前景.
- 开发新的光热剂面临着化学复杂性和生物相容性方面的挑战.
研究的目的:
- 开发一种人工智能驱动的转移学习方法,以快速识别新型光热剂.
- 确定具有优良光热性能,合成可行性和生物相容性的候选物.
主要方法:
- 利用自然语言处理和预训练模型生成基于烯酸支架的分子库.
- 使用化学和生物指标 (光热性能,合成可访问性,生物毒性) 的加权分析选候选人.
- 对精选的有前途的分子进行了逆合成分析.
主要成果:
- 通过使用人工智能成功生成了潜在光热剂的库.
- 通过严格的选,通过严格的选确定了三种具有可取性质的化合物.
- 证明了人工智能驱动的方法对光热剂发现的可行性.
结论:
- 人工智能转移学习方法显著加速光热剂的设计.
- 这种方法克服了开发生物相容和有效光热剂的传统局限性.
- 已识别的候选人显示了癌症生物成像,线粒体光疗和图像引导手术中的应用潜力.
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