用生成模型与进化算法结合，快速设计出更易压片的新型共晶。
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
- 融合深度生成模型与进化优化，自动探索化学空间。
- 在计算资源有限下仍能高效设计出目标压片性能的共晶。
- 适合药物研发人员加速新药共晶筛选。
共晶化是一种可调控有机晶体理化性质的有效方法，广泛应用于生物医药领域。本文提出GEMCODE（Generative Method for Co-crystal Design），一种基于深度生成模型与进化优化融合的自动化共晶筛选新流程，旨在更广泛地探索目标化学空间。GEMCODE可实现具有特定压片性能目标的全新共晶快速设计，这对药物开发至关重要。通过一系列实验验证与发现案例，证明该方法在真实计算约束下依然有效。此外，我们还探索了语言模型在共晶生成中的潜力。最后，GEMCODE预测出多个此前未知的共晶结构，并讨论其在加速药物研发方面的前景。
原文摘要 · Abstract (English)
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.
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