Deep Learning for Proteins Notebook Series Teaches AI for Biomolecular Structure Prediction and Design
Computational methods for predicting and designing biomolecular structures are increasingly powerful. Although previous approaches relied on physics-based modeling, modern tools (e.g., AlphaFold2 in CASP14) leverage artificial intelligence (AI) to achieve significantly improved performance. The growing effect of AI-based tools in protein science necessitates enhanced educational materials that improve AI literacy among established scientists seeking to deepen their expertise and new researchers entering the field. To address this need, we developed Deep Learning for Proteins: a series of 10 interactive notebook modules that introduce fundamental machine-learning concepts, guide users through training machine-learning models for protein-related tasks, and ultimately present cutting-edge protein structure prediction and design pipelines. By using only a web browser, learners can access state-of-the-art computational tools used by professional protein engineers that range from all-atom protein design to fine-tuning protein language models for biophysically relevant functional tasks. By increasing accessibility, this notebook series broadens participation in AI-driven protein research. The complete notebook series is publicly available at https://github.com/Graylab/DL4Proteins-notebooks .