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: