SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation,Journal of Chemical Information and Modeling

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SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2021-08-26 , DOI: 10.1021/acs.jcim.1c00601
Lulu Zheng ₁ , Bin Zhu ₂ , Zengrui Wu ₁ , Fang Liang ₂ , Minghuang Hong ₂ , Guixia Liu ₁ , Weihua Li ₁ , Guobin Ren ₂ , Yun Tang ₁

Affiliation

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/.

中文翻译：

SMINBR：专门用于预测双组分晶体形成的集成网络和化学信息学工具

药物共晶和盐等双组分晶体已被证明是改善药物理化和生物制药特性的有效策略。选择合适的分子组合来形成双组分晶体并不容易。基于网络的模型已成功用于指导共晶设计。然而，基于分子相互作用拓扑信息的传统社交网络衍生方法无法直接预测尚未观察到形成双组分晶体的新化学实体 (NCE) 的相互作用伙伴。在此，我们提出了一种有效的工具，即基于子结构分子交互网络的推荐（SMINBR），以优先考虑 NCE 的潜在交互伙伴。这在硅该工具结合了网络和化学信息学方法来弥合 NCE 和已知分子相互作用网络之间的差距。10折交叉验证和外部验证的高性能体现了模型的高准确率和良好的泛化能力。作为案例研究，阿帕替尼推荐的前 10 位共形成物均经过实验确认，并获得了一种新的阿帕替尼与对二氧苯共晶。该模型的预测能力归因于其与驱动分子间相互作用形成的互补模式的一致性。SMINBR 可以自动为目标分子推荐新的相互作用伙伴，并将成为指导共晶设计的有效工具。SMINBR 的免费网络服务器可从 http://lmmd.ecust.edu.cn/sminbr/ 获得。

更新日期：2021-09-27

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