Radboud University, Institute for Molecules and Materials, Heyendaalseweg 135, 6525, AJ, Nijmegen, The Netherlands.
Angew Chem Int Ed Engl. 2020 Nov 23;59(48):21711-21718. doi: 10.1002/anie.202009467. Epub 2020 Sep 18.
A significant amount of attention has been given to the design and synthesis of co-crystals by both industry and academia because of its potential to change a molecule's physicochemical properties. Yet, difficulties arise when searching for adequate combinations of molecules (or coformers) to form co-crystals, hampering the efficient exploration of the target's solid-state landscape. This paper reports on the application of a data-driven co-crystal prediction method based on two types of artificial neural network models and co-crystal data present in the Cambridge Structural Database. The models accept pairs of coformers and predict whether a co-crystal is likely to form. By combining the output of multiple models of both types, our approach shows to have excellent performance on the proposed co-crystal training and validation sets, and has an estimated accuracy of 80 % for molecules for which previous co-crystallization data is unavailable.
大量的关注已经给予共晶的设计和合成的工业界和学术界，因为它有可能改变一个分子的物理化学性质。然而，当寻找合适的分子（或共晶）组合形成共晶时会出现困难，阻碍了目标固态景观的有效探索。本文报告了基于两种类型的人工神经网络模型和剑桥结构数据库中存在的共晶数据的基于数据的共晶预测方法的应用。该模型接受共晶的组合，并预测是否可能形成共晶。通过结合两种类型的多个模型的输出，我们的方法在提出的共晶训练和验证集上表现出优异的性能，并且对于没有以前共晶化数据的分子，估计准确性为 80%。
