Additives which share a similar molecular structure with the crystallizing parent molecule are called tailor-made additives (TMAs). The inclusion of well-chosen TMAs often has a significant impact on crystallization behaviors. However, how TMAs affect the solution crystallization has seldom been systematically summarized in recent years. Herein this paper reviews the role of TMAs in solution crystallization. First, the effects and action mechanisms of TMAs on crystal nucleation and growth are discussed, respectively. Next, the applications of TMAs in the regulation of crystal properties including the polymorphism, crystal habit, crystal size, and chirality are introduced. Then the recent progress of molecular simulation in predicting the role of TMAs is discussed. Finally, we analyze the existing problems in this field and give an outlook on the future development. This paper is helpful and useful to readers interested in the use of TMAs to control crystallization and design crystals with desired properties.

Among the physical properties characterizing cocrystals, melting temperature is one of the primary properties. Its prediction has been done by researchers, but in the known prediction models, there are some limitations. In order to adapt to the requirements of data development further and improve the quality of prediction, two prediction models of cocrystal melting temperature, the artificial neural network (ANN(I)) and the message passing neural network (MPNN), are proposed in this paper with molecular descriptors and molecular graphs as the inputs. As for ANN(I), molecular descriptors are applied to the prediction of cocrystal melting temperature for the first time, and the importance of features is evaluated by SHapley Additive explanation. For dataset 1 established in this paper, the prediction accuracy of ANN(I) is 95.75% on the training set and 95.67% on the test set. The feature evaluation of ANN(I) reveals that GATS2v, AMR, BCUTc-1h, MLFER_BH, BCUTp-1l, and ATSC1c play major roles in predicting the melting temperature. The ANN(I) model based on molecular descriptors could explain the relationship between the molecular structure and melting temperature to some extent, but its prediction accuracy is relatively poor. Thus, the message passing neural network (MPNN), a deep learning model based on molecular graphs, is proposed. For the test sets of two datasets used in this paper, the prediction accuracy of MPNN is 99.84 and 99.63%, respectively. Also, in the data pre-processing, the MPNN extracts the structural features of molecules through molecular graphs, which could save time and workload a lot. The excellent performance suggests that the MPNN model might be a more suitable tool for the prediction of cocrystal properties in the future.