AI in science
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Geometric Deep Learning in Molecular Design
Read Full Article: Geometric Deep Learning in Molecular Design![[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.
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AI Revolutionizing Nobel-Level Discoveries
Read Full Article: AI Revolutionizing Nobel-Level Discoveries
IQ is a key factor strongly correlating with Nobel-level scientific discoveries, with Nobel laureates typically having an IQ of 150. Currently, only a small percentage of scientists possess such high IQs, but this is set to change as AI IQs are rapidly advancing. By mid-2026, AI models are expected to reach an IQ of 150, equaling human Nobel laureates, and by 2027, they could surpass even the most brilliant human minds like Einstein and Newton. This exponential increase in AI intelligence will allow for an unprecedented number of Nobel-level discoveries across various fields, potentially revolutionizing scientific, medical, and technological advancements. This matters because it could lead to a transformative era in human knowledge and problem-solving capabilities, driven by super intelligent AI.