ESM-2 Protein Language Model Explained – Architecture & Training

In this video, we introduce the ESM-2 protein language model, one of the most widely used transformer-based models for sequence-based protein prediction tasks. We explain the model architecture, Rotary Positional Embeddings (RoPE), and the self-supervised masked language modeling objective that makes ESM-2 so powerful for downstream protein property prediction. 📚References ESM-2 paper (Lin et al., Science) “Evolutionary-scale prediction of atomic-level protein structure with a language model” DOI: https://doi.org/10.1126/science.ade2574 ESM-2 Python code walkthrough & fine-tuning (Part 2 video)    • Using ESM-2 in Python: Protein Embeddings ...   Jupyter notebook for ESM-2 fine-tuning & analysis https://github.com/ProteinVision/ESM2... 🎵Credits Intro music: “Unbreakable” – Pixabay Music https://pixabay.com/music/main-title-... 🧬About ProteinVision We are ProteinVision, and we develop state-of-the-art models for protein prediction tasks. 🌐 Homepage: https://protein.vision 📧 Contact: [email protected] If you find this video helpful, please like, subscribe, and let us know in the comments which protein models or tasks you’d like to see next!