Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high‐throughput to advanced models

• Published in: Journal of computational chemistry 2024-11, Vol.45 (29), p.2465-2475
• Main Authors: Fox, Robert, Klug, Joaquin, Thompson, Damien, Reilly, Anthony
• Format: Article
• Language: English
• Subjects: Aspirin; Assemblies; Biocompatibility; Density functional theory; Enthalpy; Oxalic acid
• ISSN: 0192-8651; 1096-987X; 1096-987X
• DOI: 10.1002/jcc.27454

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4′‐bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals. To go beyond trial‐and‐error wet lab synthesis, it is important to develop computational methods that accurately capture the structure, dynamics and energetics at materials interfaces, such as the large‐area noncovalent contacts between different kinds of molecules in organic cocrystals. Our dataset tracks and quantifies systematic increase in predictability on shifting from inexpensive semi‐empirical methods to full dispersion‐inclusive DFT and may help inform model selection for future screens to identify stable cocrystal combinations for (bio)pharma applications.