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Strategy for Efficient Discovery of Cocrystals via a Network-Based Recommendation Model
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2020-08-26 , DOI: 10.1021/acs.cgd.0c00911 Lulu Zheng 1 , Bin Zhu 2 , Zengrui Wu 1 , Xiaoxue Fang 2 , Minghuang Hong 2 , Guixia Liu 1 , Weihua Li 1 , Guobin Ren 2 , Yun Tang 1
Crystal Growth & Design ( IF 3.2 ) Pub Date : 2020-08-26 , DOI: 10.1021/acs.cgd.0c00911 Lulu Zheng 1 , Bin Zhu 2 , Zengrui Wu 1 , Xiaoxue Fang 2 , Minghuang Hong 2 , Guixia Liu 1 , Weihua Li 1 , Guobin Ren 2 , Yun Tang 1
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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.
中文翻译：
通过基于网络的推荐模型有效发现共晶的策略
共晶的实验筛选通常是费力且费时的。因此，迫切需要发展有效的计算机技术预测模型来指导共晶发现。在这项研究中，提出了基于网络的推荐模型，以预测共晶网络中分子的新共晶。首次证实了局部随机游走（LRW）推荐算法是共晶设计中的有效模型。LRW的算法原理可以捕获共晶体系中的超分子合成子机制，并掌握共晶网络的结构特征，具有令人满意的预测能力。我们的模型可以区分最近文献中报道的各种药物共晶体，这证明了我们方法固有的良好泛化能力。作为案例研究，预测并随后获得了新的阿帕替尼共晶体。预测结果与实验结果之间的一致性突出了预测模型的准确性和实用性。特别是，我们的预测模型在计算时间上具有竞争力，并且易于实现。总之，我们基于网络的推荐模型将是指导实验性共晶筛选和提高共晶发现效率的有效工具。
更新日期：2020-10-07
中文翻译：
通过基于网络的推荐模型有效发现共晶的策略
共晶的实验筛选通常是费力且费时的。因此，迫切需要发展有效的计算机技术预测模型来指导共晶发现。在这项研究中，提出了基于网络的推荐模型，以预测共晶网络中分子的新共晶。首次证实了局部随机游走（LRW）推荐算法是共晶设计中的有效模型。LRW的算法原理可以捕获共晶体系中的超分子合成子机制，并掌握共晶网络的结构特征，具有令人满意的预测能力。我们的模型可以区分最近文献中报道的各种药物共晶体，这证明了我们方法固有的良好泛化能力。作为案例研究，预测并随后获得了新的阿帕替尼共晶体。预测结果与实验结果之间的一致性突出了预测模型的准确性和实用性。特别是，我们的预测模型在计算时间上具有竞争力，并且易于实现。总之，我们基于网络的推荐模型将是指导实验性共晶筛选和提高共晶发现效率的有效工具。
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