Conference Day One

8:00 am Registration & Welcome Coffee

8:45 am Chair’s Opening Remarks

Bridging the Gap between Data & Model in Computational Biology

9:00 am Harnessing Artificial Intelligence to Make Antibodies Smarter

Synopsis

  • Delivering the story of the AI-designed antibody, AU-007, from idea to in-vivo to patient
  • How can we give antibodies dynamic capabilities in humans while maintaining the developability profile?
  • What data do we need to design smarter biotherapeutics?

9:30 am Accelerating Drug Discovery and Development with a Virtual Twin Approach

Synopsis

  • Reviewing the importance of Computational Biologics Design and Development (CBDD) approaches to address the issue of multi-objective screening & multi-dimensional optimization
  • Discussing how engineering of biologics has led to an increase in the variety of formats. Optimization of biologics is becoming more complex
  • Highlighting workflows and the thought process to tackle these challenges posed by the complexity of the various antibody-based biologics

10:00 am Panel Discussion: Debating Best-Practices for Data Flow, Accumulation, & Utilisation to Provide Novel Insight into Biological Processes & Biologic Design

Synopsis

  • Reviewing approaches for validation, storage & protection of data under the FAIR principles
  • Navigating large biological data sets based on data warehouses for data integration & management provides novel insight into biological processes
  • How do we create a database that is representative of a wide variety of biological information?

10:45 am

Speed Networking & Morning Coffee

11:15 am Session Reserved for CDD Vault

Synopsis

11:45 am From Data to Predictions: AI-Based Virtual Screening for Multi- Specific Protein Therapeutics

Synopsis

  • Examining the setup of our industry-leading high-throughput engineering platform for multi-specific protein therapeutics
  • Applying AI- and structure-based in-silico design workflows to extract knowledge from huge engineering data sets and to guide the discovery &
    optimization of novel multi-specific biologics
  • Exemplifying an AI-based virtual screening approach within one of Sanofi’s multi-specific biologics optimization campaigns

12:15 pm Data-Driven Target Discovery: Leveraging Real World Human Data to Understand Disease Mechanism

  • Xin Luo Senior Scientist, Computational Biology, Amgen

Synopsis

  • How can we use real world data to understand disease mechanisms?
  • How can we leverage data to support the development of novel therapeutics?
  • Assessing in-vivo & in-vitro preclinical models in downstream screening and validation
  • Highlighting future avenues for MoA elucidation, whereby data is translated into meaningful hypothesis for novel biologics candidates through clinic

12:45 pm Enabling Robust Prediction of Protein-Protein Binding Affinities Using FEP+

  • Jared Sampson Senior Scientist, Life Sciences Software, Schrödinger

Synopsis

  • Physics-based free energy perturbation (FEP) calculations provide accurate energetics while allowing conformational flexibility by using explicit solvent molecular dynamics (MD) simulations with our state-of-the-art OPLS4 force field
  • Enhanced sampling protocols for the mutating residue and nearby waters; intelligent handling of Proline and charged amino acids; and automated parameterization of noncanonical amino acids lead to improved results
  • Both FEP+ and our new constant-pH molecular dynamics (CpHMD) implementation can account for protonation and tautomeric state changes, both upon binding/folding and at different pH values

1:15 pm

Networking Lunch Break

Accelerating Novel Biotherapeutics Discovery with Computational Biology

2:00 pm Developing a Data Management Workflow to Accelerate Candidate Selection & Reduce Risks in Development

Synopsis

  • Introducing Merck’s novel data management system, now usable for ML/AI with millions of entries
  • Integrating the Hight Throughput analytical characterization data management system “Spotfire Dashboard” to drive better decision-making when screening through a large dataset
  • Examining best practices for data selection and curation to achieve maximum impact during the early stages of antibody molecule generation

2:30 pm Applying AI to Unravel the Massive Amounts of Highly Organised Protein Data Stored in 3DM’s Protein Superfamilies Databases

Synopsis

  • What is 3DM and what makes is it unique to other methods?
  • Why is Helix, the AI platform build on 3DM, so much better in predicting the effects of Human SNPs than all other available tools?
  • How can you as customer access the predictions including all underlying data and reports that explain why a mutation is predicted to be pathogenic or not?
  • How do we apply the same strategy to speed up protein engineering projects, including optimising or humanizing antibodies?

3:00 pm Exploring a Theoretical Framework to Guide De Novo Biologic Design

Synopsis

  • Task 1: Given the antigen sequence, can you set the sequence motif of the antibody to make a reliable prediction on whether a particular antibody would bind to this antigen?
  • Task 2: Given an antibody sequence, can you identify the most likely epitope on a particular antigen if you already know that this antibody binds to this antigen?
  •  Task 3: Given a known antibody sequence that binds to a particular epitope on an antigen, how many different antibody sequences we identify that will bind to the same epitope?
  • Task 4: Given an arbitrary antigen, can we identify one or a few epitopes that can help design an antibody sequence binding to the designated epitope?

3:30 pm Predicting Antibody Promiscuity Based on Physico-Chemical Bio- Properties Through Integration Of Internal Peripheral Blood Cell (PBMC) Binding Data

Synopsis

  • Capturing existing peripheral blood mononuclear cell (PBMC) binding data sets to build models
  • Applying machine learning methods to predict antibody promiscuity (PBMC binding)
  • Identifying antibody physicochemical properties to predict and understand promiscuity for prospective antibodies

4:00 pm

Afternoon Refreshments

4:30 pm Addressing the Design & Engineering of Multi-specific, Multifunctional Antibodies to Advance the Constructs in-Vivo and into the Clinic

Synopsis

  • Discussing advances in the development of multifunctional antibodies with favourable stability, specificity, and pharmacokinetic properties
  • Exploring Emerging computational methods for enhancing the next generation of multi-specific antibodies
  • Leveraging current know-how by translating learnings from IgG1 design to multispecifics

5:00 pm Unravelling Computational Prediction of Protein–Protein Interactions

Synopsis

  • Reviewing the current tools and gaps within protein–protein complex prediction
  • How do you rank order the different poses that result from docking?
  • How is machine learning impacting the field of protein-protein interactions?
  • Examining applications of antigen-antibody docking for epitope identification/ epitope binning

5:30 pm Chair’s Closing Remarks

5:30 pm End of Day 1