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

Robert Fox, Joaquin Klug, Damien Thompson, Anthony Reilly

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2465-2475
Number of pages11
JournalJournal of Computational Chemistry
Volume45
Issue number29
DOIs
Publication statusPublished - 5 Nov 2024

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