Deep Supramolecular Language Processing for Co‐Crystal Prediction

Abstract

Abstract Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co‐crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising the pharmacological activity. However, finding promising co‐crystal pairs is resource‐intensive due to the large and diverse range of possible molecular combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co‐crystal formation by processing the “chemical language” from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. Explainable AI approaches uncovered the decision‐making process of DeepCocrystal, showing its capability to learn chemically relevant aspects of the “supramolecular language” that match experimental co‐crystallization patterns. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harnessed this capability in a challenging prospective study and successfully discovered two novel co‐crystals of diflunisal, an anti‐inflammatory drug. This study underscores the potential of deep learning—and in particular of chemical language processing—to accelerate co‐crystallization and ultimately drug development, in both academic and industrial contexts. DeepCocrystal is available as an easy‐to‐use web application at https://deepcocrystal.streamlit.app/ .