A Comprehensive Review on Theoretical Screening Methods for Pharmaceutical Cocrystals

In crystal engineering, cocrystallization has become one of the preferred methods for improving the physicochemical properties of active pharmaceutical ingredients (API). The success rate of enhancing the properties of API relies on the careful selection of the coformers, which is eventually a problematic task. Various experimental and theoretical screening methods have been reported in the literature to identify effective coformers for obtaining novel pharmaceutical cocrystals. Since solvents significantly impact cocrystal formation, the experimental techniques demand more chemical resources and are time-consuming. Thus, implementing theoretical cocrystal screening prior to experimental synthesis considerably reduces unwanted trials and manpower. This review mainly focuses on the in-silico cocrystal screening methods based on thermodynamic functions, Hansen solubility parameter, molecular electrostatic potential surface, Lattice energy, excess change in enthalpy (COSMO-RS), and molecular complementarity descriptors. Besides, the emerging machine-learning (ML) approaches for the theoretical prediction of cocrystal formation were reviewed. The fundamental concepts behind these screening methods, and recently published case studies utilizing virtual screening methods mentioned earlier were reviewed extensively. The software and ML tools developed for various cocrystal screening methods were explicitly discussed in this review.