Conference Day Two

8:50 am Chair’s Opening Remarks

Predictability for Biologics Developability & Understanding Toxicity

9:00 am Exploring Multi-Dimensional Approaches in Early Discovery Stages to Provide Best Suited Clinical Candidates


  • Highlighting this cost-effective approach to speed clinical candidate selection
  • Optimizing methods to humanize antibodies, trigger them as developable, and reduce downstream immunogenicity in humans, in a single optimization round
  • Leveraging learnings to ensure binder stability in humans

9:30 am Leveraging AI-ML Approaches in Biologics Discovery & Process Development

  • Huanyu Zhou Senior Director, Head, AI Data Science, Teva Pharmaceuticals


  • Introducing Teva Pharmaceuticals and their work
  • Applying AI-ML tools in target identification for biologics
  • Ensuring the optimal antibody process development by implementing AI-ML tools

10:00 am Exploring Computational Methods to Predict Biologics Safety

  • Matt Martin Matt Martin Global Head, Computational Safety Sciences, Pfizer


  • Addressing the importance of the 3Rs to support improving translation into humans
  • Exploring methods to overcome data shortages in a safety perspective
  • Leveraging computational modelling tools to remove survivor bias

10:30 am Predicting Immunogenicity in Biologics Beyond the Clinic Through Next- Generation Deep Learning Tools


  • Implementing omics, and data analysis method to train models for clinical candidate selection
  • Leveraging a protein language model to predict immunogenicity among other properties
  • Evaluating drug sensitivity of biologics candidates in the clinic using predictive models

11:15 am Morning Break & Networking Coffee

Preparing For Emerging Applications of Computational Approaches

11:45 am b>Panel Discussion: Preparing for the Future: Rethinking Career Pathways & Attracting Next Gen Scientific Talent to Drive Growth in the Space

  • Yao Fan Director, Protein Design & Optimization, Tectonic Therapeutic
  • Joel Karpiak Head, Protein Design & Informatics, GSK


  • Understanding the interplay between computational sciences and experimental platforms as the industry grows
  • Assessing the kind of talent that needs to be nurtured
  • Determining the kinds of infrastructure and the kinds of data required to empower this

12:15 pm Surface ID: A Deep Learning-based Molecular Descriptor & a Useful Tool for Antibody Characteristics

  • Yu Qiu Head, AI Innovation for Antibody, Sanofi


  • Explaining the importance of surface-based representation
  • Developing a geometric deep learning model based on self-supervised learning
  • Highlighting the applications of this model for surface similarity comparison, and antibody paratope-based clustering

12:45 pm Predicting mAb Subcutaneous Bioavailability from In Silico Structural Features


  • Assessing the current animal model gaps to test monoclonal antibody bioavailability
  • Addressing the importance of understanding bioavailability for mAbs administered subcutaneously
  • Leveraging in silico tools to predict bioavailability

1:15 pm Lunch Break

2:15 pm Breakout Roundtable Discussions:


This session is your opportunity to share your most pressing challenges, and work as a group to come up with solutions that you can implement right away! Each topic area will have several small groups, and each group will have 30 minutes to discuss ways to overcome barriers across biologics developability and computational methods. Groups will then share their findings with all attendees, giving you maximum exposure to new ideas!

2:30 pm 1. Discussing the Realistic Limitations of AI


  • Addressing the current excitement following the introduction of chatGPT
  • Reiterating the importance of innovation whilst ensuring new computational approaches are validated
  • Understanding the expectations of experimental and computational scientists with the current trends in the space

2. Highlighting Machine Learning Approaches to Overcome Developability Issues


  • Exploring the current methods used to balance formulation with biologics properties
  • Understanding the downstream implications of not overcoming these issues on HPC chromatography and more

3. Exploring the Exciting Models Entering the Space


  • Representation learning models
  • Active learning
  • Generative modeling

Emerging Integration of Computational Approaches with Biologics

3:00 pm Merging Antibody Developability with De Novo Design


  • Exploring the interplay between structure and sequence-based biologics design
  • Implementing machine learning tools for structure-based de novo design
  • Highlighting the data challenges that limit this

3:30 pm Applications & Limitations of Quantum Computing in Drug Discovery


  • Addressing the current limitations in hardware to support this technology
  • Understanding the rationale and strategies taken to implement this
  • Unravelling the future hopes for this technology

4:00 pm Chair’s Closing Remarks & End of Summit