From Nobel to Novel: How AI Is Redefining Molecular ModelingPublished inData Science Collective·11 min read·Just now--Figure created by Dall-E 2 via Skype.IntroductionIn 2020, Google’s DeepMind started a revolution in structural biology when it released its AlphaFold 2 model, an AI system that dramatically advanced our capability to predict the 3D structures of proteins from their amino acid sequences. This breakthrough addressed a long-standing challenge in biology, earning widespread acclaim and catalyzing further developments in the field. This wasn’t just about solving a scientific puzzle; it unlocked the potential to understand biological mechanisms at a molecular level in ways previously impossible, accelerating research across numerous areas. In fact, the revolution made Deepmind’s CEO and founder Demis Hassabis and AlphaFold efforts leader John Jumper worthy of the 2024 Nobel Prize in Chemistry.The revolution was not only about a new powerful AI-based system for predicting the 3D structures of proteins, but also about demonstrating the power of AI to learn complex physical and chemical principles directly from data, setting a precedent for tackling other complex scientific challenges.Soon after, other AI models began to appear that could do protein structure modeling and even design, as I’ve covered for some: