Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. In this work, we present Generative Method for Co-crystal Design (GEMCODE), a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast de novo co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEMCODE and discuss its potential in accelerating drug development.

翻译：共结晶是一种调控有机晶体理化性质的有效方法，在生物医学领域具有广泛应用。本研究提出了一种基于深度生成模型与进化优化相融合的新型共晶自动筛选流程——共晶设计生成方法（GEMCODE），该方法能够对目标化学空间进行更广泛的探索。GEMCODE 能够快速实现具有目标可压片性特征的共晶从头设计，这对药物开发至关重要。通过一系列突显验证与发现案例的实验研究，我们证明即使在现实计算约束下，GEMCODE 依然有效。此外，我们探索了语言模型在生成共晶方面的潜力。最后，我们展示了 GEMCODE 预测的众多先前未知的共晶，并讨论了其在加速药物开发方面的潜力。