Conference Day One

8:00 am Registration & Morning Coffee

8:50 am Chair’s Opening Remarks

  • Maria Wendt Global Head, Digital & Biologics Strategy & Innovation, Large Molecule Research, Sanofi

Bridging the Data Gap with Innovative Computational Approaches

9:00 am Plenary Panel: Navigating the Recent Trends in Computational Drug Development & Their Implications in Biologics Processes

  • Andy Vo Senior Scientist II, Computational Toxicology, Abbvie
  • Yao Fan Director, Protein Design & Optimization, Tectonic Therapeutic
  • Bin Li Director, Computational Biology, Takeda
  • Maria Wendt Global Head, Digital & Biologics Strategy & Innovation, Large Molecule Research, Sanofi


  • Reaffirming the importance of fostering collaboration to overcome the data gap
  • Assessing if insights from big tech can empower biologics decision making
  • Addressing the next steps in computational approaches to improve data integrity and diversity

9:45 am Employing a Mathematical Modeling Approach to Overcome Data Shortages


  • Presenting a case study on overcoming challenges at a stage of development
  • Spotlighting the rationale behind this process
  • Unravelling the future hopes for this approach

10:15 am Morning Break & Speed Networking


As the CDD for Biologics community is reunited, this valuable session will ensure you get the chance to reconnect with peers and make brand new connections too! This structured networking opportunity will pair you with fellow attendees for several 3-minute introductions, ensuring you have the opportunity to meet and network with your academic and industry colleagues!

11:15 am Benchmarking Computational Approaches to Support Biologics Discovery

  • Michael Marlow Director, Biologics, Chemistry, Manufacturing & Controls Research, Boehringer Ingelheim


  • Integrating computational approaches with experimental workflows to support data validation
  • Exploring the rationale to establish a baseline that balances computational and experimental approaches
  • Discovering the potential for this approach to empower biologics discovery and development

Predictability For Biologics Discovery

12:00 pm Breakout Roundtable Discussions: Exploring the Success of Computational Models Across Pipelines


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 in implementing computational models across pipelines. Groups will then share their findings with all attendees, giving you maximum exposure to new ideas!

1. Predictive Model Development:


Reaffirming and understanding the success and challenges in developing predictive models

2. Error Reduction:


Strategizing methods to reduce errors and overcome computational limitations

3. Validating in silico tools:


Developing practical methods to establish validated in silico tools to set a gold standard

12:30 pm Lunch Break

1:30 pm Implementing A Simulation Model to Predict Antibody Physical Stability


  • Highlighting the physics-based modelling approach to develop these tools
  • A case study on the applications of this simulation model
  • Leveraging key takeaways from this model to support antibody discovery

2:00 pm Spotlighting Two Approaches to Overcome the Data Gap in Bi-Specifics Discovery

  • Norbert Furtmann Group Leader, Computational & Highthroughput Protein Engineering, Sanofi


  • Exploring a high-throughput approach to generate large datasets for AI model training to improve bispecific expression predictions
  • Highlighting a computational approach (non-AI) to predict PK and clearance using a smaller dataset based on animal studies
  • Testing data from both approaches to experimentally validate the screening tool and the trained non-AI model for bispecific prediction

2:30 pm Networking Break & Poster Session


Witness some of the latest and greatest research in the computational space by drug developers, academics, and researchers in this spotlight poster session.

Predictability For Biologics Developability

3:30 pm Target Selection to Maximize Success from Your Antibody Platform


  • Analyzing the importance of target selection and optimization
  • Discover “validated” targets that leverage your platform’s competitive advantage
  • Address the value and challenges of machine learning and AI for target selection

4:00 pm Combining Molecular Modeling and Machine Learning to Predict Concentrated Antibody Solution

  • Pin-Kuang Lai Assistant Professor, Stevens Institute of Technology


  • Identify molecular descriptors to predict antibody viscosity
  • Develop fast screening tool using deep learning
  • Strategies to overcome the variations in viscosity and aggregation as the formulation changes

4:30 pm Chair’s Closing Remarks & End of Day One