![[D] I summarized my 4-year PhD on Geometric Deep Learning for Molecular Design into 3 research questions](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9563-300x171.png)
The PhD thesis explores the application of Geometric Deep Learning in molecular design, focusing on three pivotal research questions. It examines the expressivity of 3D representations through the Geometric Weisfeiler-Leman Test, the potential for unified generative models for both periodic and non-periodic systems using the All-atom Diffusion Transformer, and the capability of generative AI to design functional RNA, demonstrated by the development and wet-lab validation of gRNAde. This research highlights the transition from theoretical graph isomorphism challenges to practical applications in molecular biology, emphasizing the collaborative efforts between AI and biological sciences. Understanding these advancements is crucial for leveraging AI in scientific innovation and real-world applications.
