Advancements in Computational Screening Methods for Pharmaceutical Cocrystals

Abstract

In crystal engineering, pharmaceutical cocrystals are designed as co-systems composed of an active pharmaceutical ingredient (API) and a coformer held together by noncovalent interaction. Cocrystallization is a commonly employed technique to enhance the solubility and bioavailability of drugs. The selection of a suitable coformer has a paramount role in cocrystal development. A vast range of coformers is currently available for cocrystallization experiments. Coformer selection is either done by experimental- or computational-based methods. Experimental methods predominantly rely on the traditional empirical approach and are thus extremely tedious. Computational methods can boost the cocrystal research capabilities by providing information about the API-coformer complex even before experimentation by considering the structural features of the components. Computational-based screening methods can screen libraries of compounds in a relatively short time. Molecular electrostatic potential surfaces (MEPS), Hirshfeld surface analysis (HSA), Cambridge structure database (CSD), COSMO-RS (conductor- like screening model for real solvents), and Crystal structure prediction (CSP) are the prominent cocrystal screening tools employed by the researchers. These methods can also save significant time and resources. Apart from assisting the screening, the physical and chemical properties of formed cocrystals can also be approximated by these computational methods. This review provides insights into the latest findings and advances in computational-based cocrystal screening methodologies. © 2025 Scrivener Publishing LLC.