Engineering Coordination Cages With Generative AI

Sammanfattning

Deep learning methods applied to chemistry can speed the discovery of novel compounds and facilitate the design of highly complex structures that are both valid and have important societal applications. Here, we present a pioneering exploration into the use of Generative Artificial Intelligence (GenAI) to design coordination cages within the field of supramolecular chemistry. Specifically, the study leverages GraphINVENT, a graph-based deep generative model, to facilitate the automated generation of tetrahedral coordination cages. Through a combination of computational tools and cheminformatics, the research aims to extend the capabilities of GenAI, traditionally applied in simpler chemical contexts, to the complex and nuanced arena of coordination cages. The approach involves a variety of training strategies, including initial pre-training on a large dataset (GDB-13) followed by transfer learning targeted at generating specific coordination cage structures. Data augmentation techniques were also applied to enrich training but did not yield successful outcomes. Several other strategies were employed, including focusing on single metal ion structures to enhance model familiarity with Fe-based cages and extending training datasets with diverse molecular examples from the ChEMBL database. Despite these strategies, the models struggled to capture the complex interactions required for successful cage generation, indicating potential limitations with both the diversity of the training datasets and the model’s architectural capacity to handle the intricate chemistry of coordination cages. However, training on the organic ligands (linkers) yielded successful results, emphasizing the benefits of focusing on smaller building blocks. The lessons learned from this project are substantial. Firstly, the knowledge acquired about generative models and the complex world of supramolecular chemistry has provided a unique opportunity to understand the challenges and possibilities of applying GenAI to such a complicated field. The results obtained in this project have highlighted the need for further refinement of data handling and model training techniques, paving the way for more advanced applications in the future. Finally, this project has not only raised our understanding of the capabilities and limitations of GenAI in coordination cages, but also set a foundation for future research that could eventually lead to breakthroughs in designing novel cage structures. Further study could concentrate on learning from the linkers in future data-driven cage design projects.