Huang Wenzhi, Huang Shuai, Fang Yanpeng, Zhu Tianyu, Chu Feiyi, Liu Qianhui, Yu Kunqian, Chen Fei, Dong Jie, Zeng Wenbin
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, P. R. China.
State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, P. R. China.
Adv Sci (Weinh). 2024 Sep;11(35):e2405596. doi: 10.1002/advs.202405596. Epub 2024 Jul 17.
Excited-state intramolecular proton transfer (ESIPT) has attracted great attention in fluorescent sensors and luminescent materials due to its unique photobiological and photochemical features. However, the current structures are far from meeting the specific demands for ESIPT molecules in different scenarios; the try-and-error development method is labor-intensive and costly. Therefore, it is imperative to devise novel approaches for the exploration of promising ESIPT fluorophores. This research proposes an artificial intelligence approach aiming at exploring ESIPT molecules efficiently. The first high-quality ESIPT dataset and a multi-level prediction system are constructed that realized accurate identification of ESIPT molecules from a large number of compounds under a stepwise distinguishing from conventional molecules to fluorescent molecules and then to ESIPT molecules. Furthermore, key structural features that contributed to ESIPT are revealed by using the SHapley Additive exPlanations (SHAP) method. Then three strategies are proposed to ensure the ESIPT process while keeping good safety, pharmacokinetic properties, and novel structures. With these strategies, >700 previously unreported ESIPT molecules are screened from a large pool of 570 000 compounds. The ESIPT process and biosafety of optimal molecules are successfully validated by quantitative calculation and experiment. This novel approach is expected to bring a new paradigm for exploring ideal ESIPT molecules.
激发态分子内质子转移（ESIPT）因其独特的光生物学和光化学特性，在荧光传感器和发光材料领域备受关注。然而，目前的结构远不能满足不同场景下对ESIPT分子的特定需求；反复试验的开发方法既耗费人力又成本高昂。因此，迫切需要设计新的方法来探索有前景的ESIPT荧光团。本研究提出了一种旨在高效探索ESIPT分子的人工智能方法。构建了首个高质量的ESIPT数据集和一个多级预测系统，该系统实现了从大量化合物中准确识别ESIPT分子，其过程是从传统分子逐步区分到荧光分子，再到ESIPT分子。此外，通过使用SHapley Additive exPlanations（SHAP）方法揭示了有助于ESIPT的关键结构特征。然后提出了三种策略，以确保ESIPT过程，同时保持良好的安全性、药代动力学性质和新颖结构。通过这些策略，从570000种化合物的大库中筛选出700多种以前未报道的ESIPT分子。通过定量计算和实验成功验证了最佳分子的ESIPT过程和生物安全性。这种新方法有望为探索理想的ESIPT分子带来新的范例。
