文献类型：期刊文献

英文题名：Strategy for Efficient Discovery of Cocrystals via a Network-Based Recommendation Model

作者：Zheng, Lulu[1];Zhu, Bin[2];Wu, Zengrui[1];Fang, Xiaoxue[2];Hong, Minghuang[2];Liu, Guixia[1];Li, Weihua[1];Ren, Guobin[2];Tang, Yun[1]

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

年份：2020

卷号：20

期号：10

起止页码：6820

外文期刊名：CRYSTAL GROWTH & DESIGN

收录：;EI(收录号：20204509459421);WOS:【SCI-EXPANDED(收录号:WOS:000580511100061)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2016YFA0502304), the National Natural Science Foundation of China (Grants 81872800, 21776073 and 21908055), and China Postdoctoral Science Foundation (Grant 2019M661410).

语种：英文

摘要：Experimental screening of cocrystals is usually laborious and time-consuming; therefore, it is urgent to develop effective in silico predictive models to guide cocrystal discovery. In this study, network-based recommendation models were proposed to predict new cocrystals for molecules in cocrystal network. The local random walk (LRW) recommender algorithm was first confirmed as an effective model in cocrystal design. The algorithmic principle of LRW could capture the supramolecular synthon mechanisms in the cocrystal system and grasp the structural features of the cocrystal network, thus possessing satisfactory predictive capability. Various pharmaceutical cocrystals reported in the recent literature could be distinguished by our model, which demonstrates the good generalization capability inherent in our approach. As a case study, new cocrystals for apatinib were predicted and subsequently obtained. The consistency between prediction and experimental results highlighted the accuracy and practicability of the predictive model. Particularly, our predictive model is competitive in computational time and easy to implement. In summary, our network-based recommendation model would be an effective tool to guide experimental cocrystal screening and improve the efficiency of cocrystal discovery.