Cocrystal design by network-based link prediction

Abstract

Cocrystallization is an attractive formulation tool for tuning the physicochemical properties of a compound while not altering its molecular structure and has gained interest from both industry and academia. Although the design strategy for cocrystals has marked several milestones over the past few decades, a holistic approach that utilizes as much cocrystal data as possible is still lacking. In this paper, we describe how information contained in the Cambridge Structural Database (CSD) can be used to construct a data-driven cocrystal prediction method, based on a network of coformers and link-prediction algorithms. Experimental validation of the method leads to the discovery of ten new cocrystal structures for its top ten predictions. The prediction method is not restricted to compounds present in the CSD: by combining the information of only a few cocrystals of an unknown coformer (e.g. an API in development) together with the information contained in the database, a set of relevant cocrystal candidates can be generated.