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2025年1月13日（月） 9:00 am - 6:00 pm
TS1A: Introduction to Machine Learning for Biologics Design
バイオロジクスのデザイン向けML入門
This course offers an introduction to concepts, strategies, and machine learning methods used for biologics design. It includes presentations and demonstrations of the methods used in the field, covering techniques such as triaging sequences, modulating affinity, and designing antibody libraries, along with increasing manufacturability. The course is directed at scientists new to the field and protein engineers wanting an introduction to how machine learning can aid in guiding biologics design.
- Basics of machine learning and where does it fit into drug discovery
- Modern homology modeling and structure prediction
- Predicting antibody affinity and specificity modulation
- Generative design in biologics: library design and language models
- Machine learning applications of T-cell and B-cell Immunogenicity
- Methods and application of ML for chemical, folding, solution stabilities
INSTRUCTOR BIOGRAPHIES:
Christopher R. Corbeil, PhD, Research Officer, Human Health Therapeutics, National Research Council Canada
Dr. Christopher Corbeil is a research officer at the National Research Council Canada (NRC) who specializes in the development and application of computational tools for biotherapeutic design and optimization. He is also an associate member of the McGill Biochemistry Department and teaches classes in Structure-Based Drug Design at McGill University. After receiving his PhD from McGill University, he joined the NRC as a Research Associate investigating the basics of protein-binding affinity. Following his time at the NRC he joined Chemical Computing Group as a research scientist developing tools for protein design, structure prediction, and binding affinity prediction. He then decided to leave private industry and rejoin NRC with a focus on antibody engineering. Dr. Corbeil has authored over 30 scientific articles and is the main developer of multiple software programs.
Francis Gaudreault, PhD, Associate Research Officer, Human Health Therapeutics, National Research Council Canada
Francis obtained his PhD in Biochemistry from University of Sherbrooke in 2015, during which he developed a molecular docking program for docking small molecules to flexible protein or RNA targets. While doing his PhD studies, Francis co-founded a successful IT company for automating the management of scientific conferences. Francis joined the National Research Council (NRC) of Canada in 2016, where he has taken part in and led various efforts in the discovery and engineering of antibodies or other biologics. In such efforts are included the structure prediction of antibodies alone or in complex, the affinity assessment of antibody-antigen complexes, and the detection of antibody developability issues. Francis is leading the technical efforts in using artificial intelligence for antibody discovery.
TS2A: Implementing Artificial Intelligence and Computational Tools in Biopharmaceutical R&D
バイオ医薬品R&DにおけるAIと計算ツールの導入
Artificial intelligence and other computational techniques have revolutionized biopharma research over the past two decades, leading to the solutions of fundamental problems such as protein folding and the acceleration of numerous aspects of biopharma R&D. This seminar will survey the landscape of AI and computational tools with an emphasis on the steps needed to implement AI-based workflows and programs in biotherapeutic research organizations. We will examine case studies and interactive demonstrations in a range of application areas in which AI has led to acceleration and innovation, including identifying novel drug targets, predicting protein structure, designing small molecules and antibodies, and optimizing biopharmaceutical manufacturing processes.
Attendees do not need a deep computational background but will be introduced to cloud computing and containerized workflows in a hands-on fashion and should be familiar with basic concepts of programming and command line operations.
Topics to be covered:
- Identifying opportunities for AI/ML tools in existing and new programs
- Evaluating internal staff and experimental capabilities
- The role of ML scientists; do these need to be internal?
- Scoping, developing and sourcing training data
- Bespoke versus off-the-shelf models
- Cloud computing and containerized workflows
- Identifying drug targets in silico
- Protein structure prediction
- Antibody design and developability
- Small molecule design
- Designing de novo proteins with deep learning
INSTRUCTOR BIOGRAPHIES:
Ryan Peckner, PhD, Director, Machine Learning, Seismic Therapeutic
Ryan Peckner has been the head of machine learning at Seismic Therapeutic since early 2022, where he leads a team focused on applying ML to develop next-generation classes of non-immunogenic protein therapeutics. He earned his PhD in theoretical mathematics at Princeton University in 2015 and, after deciding to transition to an applied field, completed his postdoctoral training at the Broad Institute with an emphasis on the intersection of proteomics, genomics, and machine learning. Since entering biotech in early 2019, he has focused on developing and applying new machine learning techniques to structural biology, immunology, and drug development, beginning with models to probe TCR-pMHC interactions at Repertoire Immune Medicines and continuing with his work at Seismic.
TS3A: AI-Driven Design of Biologics
バイオロジクスにおけるAIドリブンのデザイン
Discover how to revolutionize biologics design using cutting-edge AI models for drug discovery and healthcare. In this immersive hands-on seminar, attendees will explore the applications of machine learning tools for protein structure prediction and design. Participants will navigate through practical applications using open-sourced, state-of-the-art tools such as -AlphaFold, ESMFold, ProteinMPNN, RFDiffusion, and others-all within an intuitive Jupyter notebook environment. From understanding the nuances of protein-protein docking (with tools like EquiDock, DiffDock-PP, etc) to harnessing the power of language models (ProGen, IgLM, etc), this seminar will cover a breadth of fields in protein design. Attendees will also delve into the innovative realms of hallucination and diffusion-based models for protein engineering. By the end of the seminar, participants will be equipped with the knowledge and skills to implement these AI-driven tools in their own research and development projects.
Introduction and Overview:
- High-level overview of ML tools
Structure Prediction:
- Understand the differences between structure prediction vs. design with AlphaFold and ESMFold
Protein-Protein Docking:
- Dock proteins together using DiffDock, AlphaFold3, and Boltz-1
Protein Function:
- Predict function, pockets, and stability using tools like ProtNLM, AF2BIND, and NanoMelt
Protein Design:
- Design new proteins from sequence, structure, and text using state-of-the-art tools like ESM, MP4, and RFDiffusion
INSTRUCTOR BIOGRAPHIES:
Timothy Riley, PhD, Vice President, Discovery, 310 AI
Tim received his PhD in structural and computational immunology from the University of Notre Dame under Dr. Brian Baker. There, he co-founded Structured Immunity, a company using computational strategies to design improved cell-based therapies. Afterwards, he assumed leadership and management positions at Amgen and A2 Biotherapeutics, contributing significantly to the design and development of groundbreaking therapeutics in the fields of immuno-oncology and cardiovascular diseases. Most recently, he has joined 310 AI as the VP of Discovery - leveraging their powerful AI platform to design novel biomolecules for therapeutic use.
Kathy Y. Wei, PhD, Co-Founder & CSO, 310 AI
Kathy earned her PhD in RNA synthetic biology with Christina Smolke at Stanford. She then went to a postdoc at the University of Washington in the Institute of Protein Design with David Baker, where she used Rosetta to computationally design de novo protein switches. Kathy then went to UC Berkeley to work with Daniel Fletcher, where she helped cross-pollinate the Baker and Fletcher labs. She began to apply her skills in industry at Amgen, where she led the AmgenFold team, which deploys state-of-the-art ML structure prediction methods for internal use. Now, she is co-founder and CSO for 310 AI, a generative AI company for designer biology. 310 AI believes that the design of novel biomolecules is the single largest advancement that can be enabled by AI.
* 不測の事態により、事前の予告なしにプログラムが変更される場合があります。
会議概要
MODELING AND PREDICTION STREAM
モデリング・予測ストリーム
BIOTHERAPEUTIC R&D STREAM
バイオ医薬品R&Dストリーム
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