Conference Day Two

Streamlining Drug Development with Advanced Bioinformatics

8:30 am Welcome Coffee

8:50 am Chair’s Opening Remarks

9:00 am Optimizing your Biologic’s Design for Downstream Developability


  • Contextualizing antibody developability parameters through use cases, with focus on antibody hydrophobicity and polyspecificity
  • Leveraging deep learning to learn patterns in antibody sequences and feed into developability prediction
  • Combining NGS data and deep learning to inform synthetic library designs yielding antibodies with better developability

9:30 am Panel Discussion: Using Deep & Machine Learning Methods to Develop & Optimize Therapeutic Anti-bodies


  • Revealing the pillars for ML-based optimization of therapeutic antibodies
  • Outlining recent progress in the application of ML methods for antibody optimization
  • Comparing organizational strategies for developing optimal therapeutic antibodies

10:15 am

Morning Networking & Coffee

10:45 am Optimising Antigen Specificity Using Computational Structural Modelling


  • Evaluating current antibody and antigen structure models and their contribution to structure-based design
  • Exploring challenges when applying structural analytics via a practical case study
  • How far are we from designing antibodies fully in silico?

11:15 am Driving Antibody Optimization Using a Mix of Sequence, Structure, Computation, and Screening


  • Addressing multi-goal lead optimization: Developability, immunogenicity, affinity, specificity
  • When do you design from sequence, model structure, search computationally, and/or screen libraries?
  • Discussing ATRC-101 and ATRC-301 clinical candidate optimization case studies

11:45 am Roundtable: Addressing In-Silico Prediction of Immunogenicity to Circumvent Alterations in Drug Pharmacokinetics, Pharmacodynamics, & Efficacy


  • Revealing the latest developments for in-silico immunogenicity prediction
  • Outlining examples and the different ways biologics can be immunogenic
  • In what cases are computational tools meaningful? Where do they fail?

12:30 pm

Networking Lunch Break

1:30 pm Acclerating AI and Data-driven Design of Biologics to Untap Novel Biotherapeutics


  • Examining best-practices for data-driven design of biologics by showcasing the optimization of a bispecific antibody
  • How can we best leverage AI to design novel biotherapeutics?
  • What the future hold for biologics in the age of AI?

CDD 2.0 – De-risking Your Novel Biologics Pipeline Progression

2:00 pm Applying Natural Language Processing (NLP) to Help Model Informed Biologic Development

  • Peter Clark Head, Computational Sciences & Engineering Therapeutics Discovery, Janssen


  • Review applications of NLP algorithms in different stages of drug development lifecycle through NLP use cases
  • Overcoming challenges to NLP employment, including reproducibility and explainability, to improve NLP acceptability by researchers and regulators
  • Minimising bias in NLP models to motivate wider adoption of NLP technologies

2:30 pm Panel Discussion: Facilitating End-To-End Integration of AI from Early Biologic Discovery to Late Development


  • Examining common challenges for end-to-end integration of AI within your organisations
  • How can we foster collaboration between different departments, including research, development, and CMC,
    and establish AI as a core organisational discipline?
  • What are the best-practices for incorporating integrated platforms for predictive models for from discovery to development?

3:15 pm

Afternoon Refreshments

3:45 pm Unpacking a Workflow for Automating Protractor Placement on Peptides for Enhanced Half Life

  • Tanmoy Sanyal Scientist, Computational Drug Discovery, Novo Nordisk


  • Employing crude heuristics for protractor placement on peptide scaffolds
  • Introducing a workflow that considers spurious interactions with protractors that may degrade the peptide or the interface
  • Can peptide-protractor interaction hotspots be leveraged for formulation design?

4:15 pm Leveraging Protein Language Modeling for Antibody Developability Prediction


  • Exploring the pretrained protein language models for antibody developability prediction
  • Investigating the possibility of transfer learning from pretrained protein language models to alleviate the scarcity of antibody developability data
  • Proposing a rapid and high throughput framework for antibody developability prediction using sequence information alone

4:45 pm Chair’s Closing Remarks

5:30 pm End of Summit