Cambridge Healthtech Institute’s 2nd Annual

Machine Learning for Protein Engineering

Part 1: 15 November 2023

Part 2: 16 November 2023

The two-part Machine Learning for Protein Engineering conference at PEGS Europe explores the informatics technologies and strategies driving the improvement of quality, precision, and developability of biotherapeutics via the application of artificial intelligence and machine learning. The 2023 meeting will focus on discovery and optimization campaigns via a critical examination of use cases, experimental validation procedures, integrations with legacy discovery platforms and data creation/curation. These programs will offer the PEGS Europe community a forum in which to debate successes to date in adopting this nascent family of technologies – and how research organizations can realize the potential in their significant investments in this space. This conference will demonstrate the considerable value of these techniques and emphasize the practical applications over the purely theoretical exercises. The format will be educational and interactive.

Coverage will include, but not limited to:

Use Cases and their Validation

  • Applying generative models for drug discovery 
  • Machine learning for antibody/target discovery and humanization
  • Optimizing molecules generated using traditional/non-computational methods
  • Structure-based developability

Developability and Optimization

  • Utilizing developability profiling of natural antibody repertoires
  • Developing rules and benchmarks for developability
  • Predicting antibody developability from sequence using machine learning

De novo Design

  • Case studies of de novo design failures
  • De novo design of candidates based on target/functional epitope interactions
  • De novo design of candidates based on pharmaceutical properties and manufacturability

Training Datasets and External Data Sources

  • Best practices in data creation and designing experiments to build training datasets
  • Evaluating protein database curation and quality (PDB, AlphFoldDB, Sab-DAB, OAS)
  • Federated data consortiums

Structural Modeling

  • Correlation of epitope target to function
  • Epitope and paratope prediction algorithms: Parapred, EpiPred, SPACE, Epitope3D, mmCSM-AB)
  • Experiences with Alphafold integrations and alternative structure prediction models (ABodyBuilder2, DeepH3, DeepAb, AbLooper, NanoNet)

Experimental Validation

  • Avoiding bias in experimental validation
  • Baselines and controls for evaluating ML campaigns
  • Parallel evaluations of AI/ML results against traditional platforms

Library Design and Analysis

  • AI-designed libraries based on phage, repertoire, and NGS data
  • Building and analyzing optimized libraries (developability, diversity, etc.)
  • Experimental design for ML-based library analysis

Challenges and Solutions

  • De novo CDR design and interrogation of CDR dynamics
  • ML as a tool for biotherapeutic design (bi/multi-specifics, novel binding scaffolds, mRNA)
  • Overcoming the obstacles to de novo design of full-length antibodies

Discussion Table Themes

  • Bench scientists: Using ML tools to guide and accelerate labwork
  • Best practices for development and curation of training datasets
  • Machine learning for the skeptic, is it falling short of its promise?
  • Small company ML implementations: early applications, when to expand/commit, relationships with external partners and data sources, building internal training datasets

The deadline for priority consideration is 31 March 2023.

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


For sponsorship information, please contact:

Companies A-K
Jason Gerardi
Sr. Manager, Business Development
Cambridge Healthtech Institute
Phone: (+1) 781-972-5452

Companies L-Z
Ashley Parsons
Manager, Business Development
Cambridge Healthtech Institute
Phone: (+1) 781-972-1340