| Autor: |
Yihang Hao, Haofan Wang, Xianggen Liu, Wenrui Gai, Shilong Hu, Wencheng Liu, Zhuang Miao, Yu Gan, Xianghua Yu, Rongjia Shi, Yongzhen Tan, Ting Kang, Ao Hai, Yi Zhao, Yihang Fu, Yaling Tang, Ling Ye, Jin Liu, Xinhua Liang, Bowen Ke |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Acta Pharmaceutica Sinica B, Vol 14, Iss 7, Pp 3086-3109 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2211-3835 |
| DOI: |
10.1016/j.apsb.2024.01.019 |
| Popis: |
Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects. The primary challenge is to integrate diverse pharmacophores within a single-molecule framework. To address this, we introduced DeepSA, a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine, a well-known local anesthetic. DeepSA integrates deep neural networks into metaheuristics, effectively constraining molecular space during compound generation. This framework employs a sophisticated objective function that accounts for scaffold preservation, anti-inflammatory properties, and covalent constraints. Through a sequence of local editing to navigate the molecular space, DeepSA successfully identified AT-17, a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models. Mechanistic insights into AT-17 revealed its dual mode of action: selective inhibition of NaV1.7 and 1.8 channels, contributing to its prolonged local anesthetic effects, and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway. These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery, particularly within stringent chemical constraints. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
