Open Rosetta

LXCL:Covers 5 days of instruction, 6 nights of double-occupancy lodging, conference room/AV, and an end-of-week group meal. Airfare, transportation, and other meals are not included.

Registration & Lodging

Recent machine learning tools for protein modeling and design operate largely outside the traditional C++ Rosetta codebase but are highly relevant to our community.This bootcamp introduces a practical selection of ML-based tools through hands-on exercises. You will learn what each tool does, its strengths and weaknesses, and when to use it. The course is aimed at novices: you will not build or train models but will focus on applying existing tools. Who is it for Graduate students and researchers with Python, PyMol, and command line proficiency, cluster access, and an interest in learning ML-based tools for protein modeling and design. Ideal for those early in their graduate career with limited prior ML experience or lab support. What you&rsquo;ll learn ✔ High-level understanding of ML-based modeling and design tools ✔ Practical use of AlphaFold2/3, OpenFold, and ESM Fold for structure prediction ✔ Backbone generation with RFDiffusion and All Atom Diffusion ✔ Sequence prediction/design using MPNN and Ligand MPNN ✔ Docking with AlphaFold3 ✔ Skills to identify appropriate ML-based methods for your projects ✔ Ability to adapt tools, run analyses, and troubleshoot common errors By the end, you will be able to select appropriate ML-based methods for your projects, run them effectively, and troubleshoot common issues&mdash;in short, apply existing tools to your own protein design research. More info here: <a data-fr-linked="true" fr-original-style="" href="https://sweltering-waterfall-a22.notion.site/ML-Bootcamp-2025-Rosetta-Sponsored-23932ee75035808193c7e5b92d8e5ba5" id="isPasted" rel="noopener noreferrer" style="text-decoration-color: initial; user-select: auto; box-sizing: inherit;" target="_blank">https://sweltering-waterfall-a22.notion.site/ML-Bootcamp-2025-Rosetta-Sponsored-23932ee75035808193c7e5b92d8e5ba5</a>

Hi <span contenteditable="false" data-custom-variable="{{firstname}}" fr-original-class="fr-deletable styled-tag-chip" fr-original-style="" style="background-color: rgb(248, 248, 255); border: 1px solid rgb(191, 191, 255); border-radius: 4px; color: rgb(42, 42, 196); padding: 1px 8px; font-size: 11px; display: inline-block; margin: 0px; overflow-wrap: anywhere; user-select: none; box-sizing: inherit;">First name, Thank you for registering for our upcoming bootcamp. We&rsquo;re excited to have you join us for five days of hands-on learning with ML-based protein modeling and design tools at UC Davis. Your registration includes instruction, six nights of double-occupancy lodging, conference room/AV, and our end-of-week group meal. Please remember to arrange your own travel and daily meals. We&rsquo;ll be in touch with more details about pre-bootcamp homework and required software setup. In the meantime, if you have any questions, feel free to reach out. We look forward to seeing you at the bootcamp! Best,Ashley Vater

Recent machine learning tools for protein modeling and design operate largely outside the traditional C++ Rosetta codebase but are highly relevant to our community.This bootcamp introduces a practical selection of ML-based tools through hands-on exercises. You will learn what each tool does, its strengths and weaknesses, and when to use it. The course is aimed at novices: you will not build or train models but will focus on applying existing tools. Who is it for Graduate students and researchers with Python, PyMol, and command line proficiency, cluster access, and an interest in learning ML-based tools for protein modeling and design. Ideal for those early in their graduate career with limited prior ML experience or lab support. What you’ll learn ✔ High-level understanding of ML-based modeling and design tools ✔ Practical use of AlphaFold2/3, OpenFold, and ESM Fold for structure prediction ✔ Backbone generation with RFDiffusion and All Atom Diffusion ✔ Sequence prediction/design using MPNN and Ligand MPNN ✔ Docking with AlphaFold3 ✔ Skills to identify appropriate ML-based methods for your projects ✔ Ability to adapt tools, run analyses, and troubleshoot common errors By the end, you will be able to select appropriate ML-based methods for your projects, run them effectively, and troubleshoot common issues—in short, apply existing tools to your own protein design research. More info here: <a href="https://sweltering-waterfall-a22.notion.site/ML-Bootcamp-2025-Rosetta-Sponsored-23932ee75035808193c7e5b92d8e5ba5" target="_blank">https://sweltering-waterfall-a22.notion.site/ML-Bootcamp-2025-Rosetta-Sponsored-23932ee75035808193c7e5b92d8e5ba5</a>

Open Rosetta

Rosetta Commons ML Bootcamp 2025

University of California-Davis

Registration & Lodging

$1,390