共晶
组分(热力学)
化学信息学
计算机科学
数据挖掘
药物数据库
交互网络
虚拟筛选
化学
分子
分子动力学
计算化学
药品
生物
氢键
药理学
热力学
基因
物理
生物化学
有机化学
作者
Lulu Zheng,Bin Zhu,Zengrui Wu,Fang Liang,Minghuang Hong,Guixia Liu,Weihua Li,Guobin Ren,Yun Tang
标识
DOI:10.1021/acs.jcim.1c00601
摘要
Two-component crystals such as pharmaceutical cocrystals and salts have been proven as an effective strategy to improve physicochemical and biopharmaceutical properties of drugs. It is not easy to select proper molecular combinations to form two-component crystals. The network-based models have been successfully utilized to guide cocrystal design. Yet, the traditional social network-derived methods based on molecular-interaction topology information cannot directly predict interaction partners for new chemical entities (NCEs) that have not been observed to form two-component crystals. Herein, we proposed an effective tool, namely substructure-molecular-interaction network-based recommendation (SMINBR), to prioritize potential interaction partners for NCEs. This in silico tool incorporates network and chemoinformatics methods to bridge the gap between NCEs and known molecular-interaction network. The high performance of 10-fold cross validation and external validation shows the high accuracy and good generalization capability of the model. As a case study, top 10 recommended coformers for apatinib were all experimentally confirmed and a new apatinib cocrystal with paradioxybenzene was obtained. The predictive capability of the model attributes to its accordance with complementary patterns driving the formation of intermolecular interactions. SMINBR could automatically recommend new interaction partners for a target molecule, and would be an effective tool to guide cocrystal design. A free web server for SMINBR is available at http://lmmd.ecust.edu.cn/sminbr/.
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