Training Seminars

Training Seminars Will Be Held In-Person Only

To ensure a cohesive and focused learning environment, moving between conference sessions and the training seminars is not allowed

Monday, January 19, 2026 8:30 am - 5:00 pm

TS7A: AI-Driven Design of Biologics: A Hands-On Guide to Using State-of-the-Art ML Protein Models

Detailed Agenda

Since 2021, artificial intelligence models have revolutionized AI-driven biologics development, enabling breakthroughs in structure prediction, sequence design, and protein engineering. This course equips researchers and professionals with the expertise to leverage cutting-edge tools for structure prediction (AlphaFold, ImmuneBuilder), protein engineering with protein language models (ESM, AntiBERTy), and structure-based design (ProteinMPNN and RFDiffusion). Through a blend of lectures and hands-on exercises, participants will learn best practices for tool selection, method optimization, and design selection. By exploring real-world applications and emerging techniques, such as BindCraft and RFAntibody, attendees will gain a practical understanding of performance capabilities, limitations, and effective workflows.

David P. Nannemann, PhD, Vice President, Rosetta Commons Foundation

Jannis de Riz, Graduate Student, University of Leipzig

Participants are expected to have some prior exposure to computational modelling tools (e.g. Python, R, COOT, Rosetta, AutoDock Vina, etc.) but limited experience applying them to their projects. They should be comfortable using Jupyter notebooks and prepared to explore topics such as evaluating metrics, determining appropriate sampling sizes, and selecting key adjustable parameters. While this seminar does not cover ligand docking or protein-protein docking, it is well-suited for those interested in antibody modeling and, potentially, enzyme design language models.

Hands-on instructional content will be presented as Google Colab notebooks written in python. A basic understanding of general coding principles, such as typing, loops, functions, and classes, will be sufficient. It will not be required to write your own code from scratch, but a sufficient familiarity with python to understand and edit the provided notebooks will be essential to a meaningful experience.

Topics to be covered:

Building practical experience with AI-based modelling of proteins
A breakdown of input formats, command lines, and analysis of output
Hands-on exercises using real-world scenarios in antibody structure prediction, developability pre-screening, immunogen solubilization, and de novo binder design
Discussion of, and guidance on, questions like: how many models, in silico selection metrics and ranking, and how many to test in the lab
Pipelining of protein design software and the critical use of an “oracle”

INSTRUCTOR BIOGRAPHIES:

Jannis de Riz, Graduate Student, University of Leipzig

I am a PhD student co-advised by Torben Schiffner and Jens Meiler. I work on Computational and experimental High-Throughput Characterization of Protein-Protein Interactions.

David P. Nannemann, PhD, Vice President, Rosetta Commons Foundation

David is an expert in protein engineering and computational design, with extensive experience applying AI-driven modeling tools in an industry setting. He serves as Vice President of the Rosetta Commons Foundation and Industry Chair on the Rosetta Commons board, helping bridge academic advancements with industry applications. As Managing Member of Rosetta Design Group, he collaborates with companies of all sizes to tackle complex challenges in biologics design. David's deep expertise in leveraging cutting-edge tools like Rosetta, AlphaFold, and diffusion-based models for protein design make him an invaluable guide for participants looking to apply AI-driven biologics design in real-world settings.

TS8A: Introduction to Antibody Engineering for ML/AI Scientists

Detailed Agenda

In this training seminar, designed for ML/AI students wishing to learn more about antibodies, students will learn about antibody basics, including the antibody therapeutic market, structure, genetics, and the generation of diversity, as well as the generation of potential therapeutic antibodies. This latter part will include antibody humanization, affinity and specificity maturation, display technologies, creation of naïve libraries, and antibody characterization. The seminar will be fully interactive with students providing ample opportunities to discuss technology with instructors.

Andrew R.M. Bradbury, MD, PhD, CSO, Specifica, an IQVIA business

James D. Marks, MD, PhD, Professor and Vice-Chairman, Department of Anesthesia and Perioperative Care, UCSF

Topics to be covered:

Antibody Background

Structure
Genes
Generation of diversity (recombination, somatic hypermutations)

Antibody Humanization

Closest human gene approach
Minimal modification approach
Veneering

Display Technologies Overview

Phage
Yeast
Combining phage and yeast display
Ribosome
Others

Generation of Naïve Antibody Libraries

Natural libraries (methods, quality control)
Synthetic libraries (including strategies for generation diversity)
Affinity Maturation
Error-prone PCR
Chain shuffling
CDR-targeted mutations

Next-Generation Sequencing in Antibody Engineering

Platforms: advantages and disadvantages
Error rates and why they are important
Naïve library diversity analysis
Selection analysis

Antibody Characterization and Developability

Expression
Specificity
Aggregation
Solubility

INSTRUCTOR BIOGRAPHIES:

Andrew R.M. Bradbury, MD, PhD, CSO, Specifica, an IQVIA business

Andrew Bradbury is Chief Scientific Officer of Specifica. He trained in medicine at the universities of Oxford and London and received his PhD from the university of Cambridge at the MRC Laboratory of Molecular Biology under the guidance of Nobel Laureate, Cesar Milstein. He has worked in the fields of phage and yeast display, library generation, antibody engineering and Next Generation Sequencing for over thirty years. He was a Group Leader at Los Alamos National Laboratory before founding Specifica. Specifica's mission is to enable companies developing therapeutic antibodies with the world’s best antibody discovery platform.

James D. Marks, MD, PhD, Professor and Vice-Chairman, Department of Anesthesia and Perioperative Care, UCSF

Dr. Marks is Professor and Vice-Chairman of the Department of Anesthesia and Perioperative Care at the University of California, San Francisco (UCSF) and Chief of Performance Excellence at Zuckerberg San Francisco General Hospital and Trauma Center (ZSFG). Dr. Marks received his medical degree from UCSF where he also completed residencies in Internal Medicine and Anesthesia and a fellowship in Critical Care Medicine. He received his Ph.D. in molecular biology from the Medical Research Council Laboratory of Molecular Biology in Cambridge, England. Dr. Marks is an internationally recognized pioneer in the field of antibody engineering, has had constant federal funding for 27 years and has authored more than 200 publications and 100 patents. In recognition of these scholarly achievements, he was elected to the National Academy of Medicine. As an entrepreneur, he has co-founded four biotechnology companies and currently serves on three biotechnology corporate boards.

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JANUARY 19 - 20

JANUARY 20 - 21

Predicting Developability and Optimization Using AI