Prediction of Hydrate and Solvate Formation Using Statistical Models

Abstract

Novel, knowledge-based models for the prediction of hydrate and solvate formation are introduced, which require only the molecular formula as input. A dataset of more than 19,000 organic, non-ionic and non-polymeric molecules was extracted from the Cambridge Structural Database. Molecules that formed solvates were compared with those that did not using molecular descriptors and statistical methods, which allowed the identification of chemical properties that contribute to solvate formation. The study was conducted for five types of solvates: ethanol, methanol, dichloromethane, chloroform and water solvates. The identified properties were all related to the size and branching of the molecules and to the hydrogen bonding ability of the molecules. The corresponding molecular descriptors were used to fit logistic regression models to predict the probability of any given molecule to form a solvate. The established models were 2 able to predict the behavior of ~80% of the data correctly using only two descriptors in the predictive model.

Why Do Some Molecules Form Hydrates or Solvates?

“…An alternative approach that addresses the posed question somewhat better has attempted to link molecular features to propensity for hydrate formation. A series of surveys of the Cambridge Structural Database (CSD) have revealed a strong correlation with the polar surface area and degree of branching within a molecule and with an increased number of polar functional groups (such as carbonyl (C=O), ether (C-O-C), hydroxyl (O-H) and primary amine (N-H)) [16][17][18] . This suggests that a strong affinity for the solvent may be important and yet there are many examples of substances with high solubility (i.e.…”

Towards an understanding of the propensity for crystalline hydrate formation by molecular compounds

“…Incorporation of water molecules in the crystal lattice is presumed to provide alternative modes of crystal packing through watermediated supramolecular heterosynthons (Clarke et al, 2010;Walsh et al, 2003). Computational and statistical models have also been used to rationalize hydrate formation (Hulme & Price, 2007;Price, 2008;Braun et al, 2011;Takieddin et al, 2016). Electrostatic potential has been shown to be an effective indicator to predict the hydrate propensity in certain types of molecules Galabov et al, 2003).…”

Synthonic Modeling of Quercetin and Its Hydrates: Explaining Crystallization Behavior in Terms of Molecular Conformation and Crystal Packing

“…2 Understanding organic molecules propensity to form hydrates and mapping their thermodynamic stability is of critical importance when formulating particulate products, particularly for the pharmaceutical, food and agrochemical industries. 3 Exposing anhydrous structures to conditions of high relative humidity can induce hydration, whilst some hydrates precipitate in water or aqueous solutions, after the dissolution of the formulated product in the desired media (stomach or digestive tract for drugs, wet soil for agrochemicals). 4 Since hydrates can present significantly different physical and chemical properties (solubility, density, bioavailability etc.)…”

“…5-3 7 In some cases the hydrated forms of molecular crystals exhibit properties that are desired for a particulate product, for example improved release rate or higher stability. 3,[7][8][9] Understanding how the water interacts with the host molecules in crystalline solids, as well as how the pathway from solution to hydrated structure can become preferential over the pathway to pure form, is essential to predict the relative stability and crystallisability of hydrated crystal forms. 1 Different crystal forms, whether they are single or multi component, can vary in terms of molecular conformation or crystal packing.…”