# Cocrystal Prediction Using Machine Learning Models and Descriptors
## 1 Introduction  
Active pharmaceutical ingredients (APIs) are commonly formulated and delivered to patients in the solid dosage forms (tablets, capsules, powders) for reasons of economy, stability, and convenience of intake [1]. One of the major problems faced during the formulation of drug is its low bioavailability which is mainly reliant on the solubility and permeability of API [2, 3], and one of the approaches to enhance the physicochemical and pharmacological properties of API without modifying its intrinsic chemical structure is to develop novel solid forms such as cocrystals [4, 5, 6, 7]. There are extensive reports on cocrystals for the purpose of improving the pharmaceutical properties including dissolution, permeability, bioavailability, stability, photostability, hygroscopicity, and compressibility [8, 9]. Cocrystals are much of interest in industrial application as well as academic research because they offer various opportunities for intellectual property rights in respect of the development of new solid forms [10]. Furthermore, the latest Food and Drug Administration (FDA) guidance on pharmaceutical cocrystals, which recognizes cocrystals as drug substances, provides an excellent opportunity for the pharmaceutical industry to develop commercial products of cocrystals [11, 12]. According to FDA, cocrystals are \"Crystalline materials composed of two or more molecules within the same crystal lattice\" [13]. Pharmaceutical cocrystals, a subclass of cocrystals, are stoichiometric molecular complexes composed of APIs and pharmaceutically acceptable coformers held together bynon-covalent interactions such as hydrogen bonding within the same crystal lattice [14]. Coformers for pharmaceutical corystallization should be from the FDA's list of Everything Added to Food in the United States (EAFUS) or from the Generally Recognized as Safe list (GRAS), as they should have no adverse or pharmacological toxic effects [1]. The list of acceptable co-formers, in principle, is likely to at least extend into the hundreds, which means that screening of suitable coformers for an API is the most crucial and challenging step in cocrystal development [15]. Since experimental determination of cocrystals is time-consuming, costly, and labor-intensive, it is valuable to develop complementary tools that can reduce the list of coformers by predicting which coformers are likely to form cocrystals [16]. Various knowledge-based and computational approaches have been used in the literature to predict cocrystal formation. Supramolecular synthesis introduced by [PERSON] is a well-known approach to rationalize the possibility of cocrystal formation [17; 18; 19]. A common strategy in this method is to first identify the crystal structure of the target molecule and investigate coformers with a desired functional group which can form intermolecular interactions (mainly hydrogen bonding) between the target molecule and coformers [15]. Knowledge of synthons allows the selection of potential coformers and predicts the interaction outcomes, but there is no guarantee that cocrystals with predicted structures would form. Statistical analysis of cocrystal data from the Cambridge Structural Database (CSD) where more than one million crystal structures of small molecules are available, allows researchers to apply virtual screening techniques to find suitable cocrystal-forming pairs [20]. [PERSON] et al. introduced hydrogen-bond propensity as a predictive tool and determined the likelihood of co-crystal formation [15; 21]. [PERSON] analyzed the possibility of cocrystal formation by correlating the different descriptors such as polarity and molecular shape [22].