解锁组件级化学结构信息,用于人工智能驱动的向纳米粒子设计.
Haomin Wu1, Yu Cheng2, Yuanhui Ji1
1Southeast University, Suzhou Research Institute, Jiangsu Provincial Key Laboratory of Brain Science and Medicine, School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, PR China.
概括
人工智能 (AI) 模型现在可以使用新的组件级图像基础结构描述器 (CISD) 预测纳米粒子生物分布. 这种方法克服了纳米粒子表示的局限性,改进了设计,并可能减少动物试验.
科学领域:
- 纳米技术 纳米技术
- 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:
- 纳米粒子的结构复杂性和不可预测的体内行为阻碍了目标设计.
- 人工智能 (AI) 提供了建立纳米粒子结构-属性关系的潜力.
- 目前的人工智能模型缺乏有效的纳米粒子组成表示,限制了预测准确性.
研究的目的:
- 开发一种用于纳米粒子组成表示的新框架.
- 将基于图像的结构描述器 (CISD) 集成到AI模型中,用于生物分布预测.
- 为了提高人工智能驱动的纳米粒子设计的准确性和可解释性.
主要方法:
- 开发了基于图像的结构描述器 (CISD),用于纳米粒子组成.
- 将CISD集成到基于AI的纳米粒子生物分布预测模型中.
- 使用SHAP分析和子函数来识别结构-属性关系.
主要成果:
- 与CISD集成的AI模型显著优于传统框架 (R2增加0.25,RMSE减少3.22).
- 确定了多层次的结构-属性关系,增强了纳米粒子设计中的可调节因素.
- 在预测的生物分布值中实现了高达12.66倍的增强.
结论:
- CISD提供了化学结构和机器可学习的纳米粒子表示之间的关键联系.
- 开发的框架提高了人工智能模型对纳米粒子设计的概括性和解释性.
- 未来与生成性AI的整合可以实现闭环反向设计,减少动物实验和加速发现.
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