# Cocrystal Prediction Using Machine Learning Models and Descriptors
## 1 Introduction
Cocrystal design can also be based on computational approaches, including the use of the \(\Delta\)pK value [23; 24], lattice energy calculation [25; 26; 27; 28], molecular electrostatic potential surfaces (MEPS) calculation [29; 30; 31; 32], Hansen solubility parameters calculation [33; 34] and Conductor like screening Model for Real solvents (COSMO-RS) based enthalpy of mixing calculation [35; 36; 37; 38]. In recent years, machine-learning (ML) has emerged as promising tool for data-driven predictions in pharmaceutical research, such as quantitative structure-activity/property relationships (QSAR/QSPR), drug-drug interactions, drug repurposing and pharmacogenomics [39]. In the area of pharmaceutical corystal research, [PERSON] et al. applied artificial neural network to predict three solid-state properties of cocrystals, including melting temperature, lattice energy, and crystal density [40]. [PERSON] et al. developed cocrystal screening models based on simple classification regression and Multivariate Adaptive Regression Splines (MARSplines) algorithm using molecular descriptors for phenolic acid coformers and dicarboxylic acid coformers, respectively [41]. [PERSON] et al. created a predictive model, that can classify a pair of coformers as a possible cocrystal or not, using a support vector machine (SVM) and simple descriptors of coformer molecules to guide the selection of coformers in the discovery of new cocrystals [16]. [PERSON] and co-workers introduced a comprehensive approach to study cocrystallization using network science and linkage prediction algorithms and constructed a data-driven co-crystal prediction tool with co-crystal data extracted from the CSD [42]. [PERSON] et al. also used a data set with co-crystal data available in the CSD and ultimately developed a machine learning model using different model types and molecular fingerprints that can be used to select appropriate coformers for a target molecule [43]. The above existing studies have shown successful results, but they have a common limitation that they only compared model performance (e.g., accuracy) without investigating features (i.e., descriptors) importance. In this work, we develop a model to predict co-crystal formation of API molecules. We use Mordred [44], one of widely-used descriptor calculators, to extract descriptor values from simplified molecular-input line-entry system (SMILES) strings of API and coformers compounds. There are several other tools or molecular descriptor-calculators used in cheminformatics such as PaDEL [45], PyDPI [46], Rcpi [47], Dragon [48], BlueDesc ([http://www.ra.cs.uni-tuebingen.de/software/bluedesc](http://www.ra.cs.uni-tuebingen.de/software/bluedesc)) and cinfony [49]; PaDEL descriptor-calculator is the most well-known tool and provides 1875 descriptors, and cinfony is a collection or a wrapper of other libraries such as Open Babel [50], RDKit ([[http://www.rdkit.org](http://www.rdkit.org)]([http://www.rdkit.org](http://www.rdkit.org))), and Chemistry Development Kit (CDK) [51]. We chose to use Mordred and used the descriptor values as features and compared different machine learning models through experimental results using our collected data. Our contributions can be summarized as follows. First, we not only extract descriptor values of compound pairs, but also investigate which descriptors are more important, and show that we can achieve good performance even if we use only a small subset of the descriptors. Second, we compare machine learning models through experiments and find that artificial neural networks (ANN) achieve the best performance. Third, we make our dataset available for free through the online website ([[http://ant.sch.ac.kr/](http://ant.sch.ac.kr/)]([http://ant.sch.ac.kr/](http://ant.sch.ac.kr/))) so that many other researchers can use the dataset as a benchmark. We believe that this study will advance the field of cocrystal formation prediction, and our dataset will help other researchers to easily develop better models.