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可解释的人工智能解读了扭曲的分子内电荷转移和平面的分子内电荷转移光体的化学结构本质

Shuai Huang¹, Wenzhi Huang¹, Yanpeng Fang¹

¹Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P. R. China.

Research (Washington, D.C.)

|December 11, 2025

概括

人工智能 (AI) 规则是为了理解扭曲 (TICT) 和平面 (PICT) 内部分子电荷转移光体的结构本质而开发的. 这些由人工智能指导的规则提高了用于生命科学和医学的光探针的设计精度.

科学领域:

有机化学有机化学
计算化学是一种计算化学.
生物医学工程生物医学工程

背景情况:

D-π-A型光材料在生命科学和医学中至关重要.
了解扭曲分子内电荷转移 (TICT) 和平面分子内电荷转移 (PICT) 机制对于光体的发展至关重要.
目前对TICT/PICT结构本质的理解有限,阻碍了光探针设计.

研究的目的:

使用可解释的机器学习来解构TICT和PICT光体的化学结构本质.
开发用于TICT和PICT光体行为的准确和可解释的预测模型.
建立人工智能引导的规则,以合理设计光探针.

主要方法:

使用可解释机器学习设计计算策略.
利用了第一个现实世界的ICT和PICT数据集.
构建预测模型平衡可解释性和准确性 (AUROC = 0.846) 与包括深度学习在内的算法.

主要成果:

建立了基于5个结构因素的AI指导规则:电子捐赠/提取组强度,基循环,固态阻碍和溶剂-溶液相互作用.
与社区建议的规则相比,人工智能引导的规则的准确性提高了20%以上.
通过使用人工智能引导的规则成功合成和验证了新型光体,证实了模型的准确性和实用性.

结论:

由人工智能指导的规则为设计基于分子刚性和电荷转移驱动力的光探针提供了明确的指导.
这种由人工智能驱动的新方法为探索和设计理想的TICT和PICT分子建立了新的范式.
为未来的研究和光材料在各种科学领域的应用提供了坚实的框架.

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