文献类型：期刊文献

英文题名：Cocry-pred: A Dynamic Resource Propagation Method for Cocrystal Prediction

作者：Song, Wenxiang[1];Peng, Ren[2];Yu, Hongbo[1];Zhan, Meiling[1];Liu, Guixia[1];Li, Weihua[1];Ren, Guobin[1,2];Zhu, Bin[1,2];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Pharmaceut Proc Chem, Sch Pharm, State Key Lab Bioreactor Engn,Minist Educ,Lab Phar, Shanghai 200237, Peoples R China

年份：2025

卷号：65

期号：6

起止页码：2868

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20251118041895);WOS:【SCI-EXPANDED(收录号:WOS:001445131300001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grants U23A20530, 22078094, 21908055), the China Postdoctoral Science Foundation (Grants 2021M701188, 2019M661410), and Suzhou Social Development Science and Technology Innovation Program (Grant 2022SS27).

语种：英文

摘要：Drug cocrystallization is a powerful strategy to enhance drug properties by modifying their physicochemical characteristics without altering their chemical structure. However, the identification of suitable coformers remains a challenging and resource-intensive task. To streamline this process, we developed a novel cocrystal prediction model, Cocry-pred, which utilizes the Network-Based Inference (NBI) algorithm-a dynamic resource propagation method-to recommend coformers for target molecules based on topological data from cocrystal network and molecular substructure information. We evaluated the impact of 13 types of molecular fingerprints and different numbers of propagation rounds on model performance. Additionally, to achieve optimal performance, we introduced three key hyperparameters-alpha (node weights), beta (edge weights) and gamma (penalty for high-degree nodes)-to balance the influence of various factors within the composite network. The best performance of Cocry-pred achieved an impressive AUC of 0.885 and an RS of 0.108. To validate the reliability of the model, we employed it to predict potential coformers for Apatinib. Subsequently, seven Apatinib cocrystals were then synthesized experimentally, among which single-crystal structures were obtained for two cocrystals. This advancement highlights the potential of Cocry-pred as a powerful tool, offering significant improvements in efficiency and providing valuable insights for cocrystal screening and design.