Cambridge Healthtech Institute’s 2nd Annual

Intelligent Antibody Discovery: Part Two

Implementing and Integrating Machine Learning in Antibody Discovery

January 17-18, 2023

 

Peptalk’s two-part Intelligent Antibody Discovery conference explores the technologies, informatics, and strategies driving a move to improve the quality, precision, and developability of biotherapeutic selections – all at very high throughput. Part One examines new capabilities of NGS and repertoire sequencing tools, strategies for developing and implementing high throughput functional assays, and the challenges of using outputs from these studies in next-generation computational models based on AI and machine learning. Part Two then builds on this foundation to consider current and near-term applications of machine learning in antibody discovery – and offers insights into implementing these tools in a discovery operation and the wet lab validation of predicted sequences and structures. The program also examines the status of efforts to apply these tools to the de novo design of antibodies and other therapeutic proteins.  

 

Coverage will include, but is not limited to:

 

Machine Learning Case Studies

  • Accelerating lead identification with affinity-matured libraries
  • Developability screening and identification of molecular liabilities
  • Machine learning for discovery of functional antibodies

 

Next-Generation ML Applications

  • Epitope-specific targeting
  • Integrating structural knowledge and biology (combining directed evolution and AI)
  • Multi-parameter lead identification and optimization

 

Experimental Validation

  • Case studies of experimental validation workflows
  • Production and validation of computationally designed libraries
  • Validation strategies for ML models

 

Applications of ML in Structure-Based Design

  • AI models for rapid structure prediction
  • AI models to predict the structural basis of antibody-antigen interactions
  • High-throughput AI-assisted, cryo-EM image analysis

 

Toward de novo Design

  • Applying lessons from de novo designs of small binding proteins to full-length antibodies
  • Case studies of alphafold-designed binders for specific epitopes
  • Near term (and feasible) applications of de novo design principals

 

Near-Term Implementation Challenges

  • Best practices for data capture, structure, curation
  • Considerations for building a machine learning infrastructure
  • Structuring and implementing training datasets

 

The deadline for priority consideration is July 1, 2022.

 

All proposals are subject to review by session chairpersons and/or the Scientific Advisory Committee to ensure the overall quality of the conference program. Additionally, as per Cambridge Healthtech Institute’s policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.

 

Opportunities for Participation:

 

 

For more details on the conference, please contact:

Kent Simmons

Senior Conference Director

Cambridge Healthtech Institute

Phone: +1 207-329-2964

Email: ksimmons@healthtech.com

 

For sponsorship information, please contact:

 

Companies A-K

Jason Gerardi

Sr. Manager, Business Development

Cambridge Healthtech Institute

Phone: +1 781-972-5452

Email: jgerardi@healthtech.com

 

Companies L-Z

Ashley Parsons

Manager, Business Development

Cambridge Healthtech Institute

Phone: +1 781-972-1340

Email: ashleyparsons@healthtech.com