Conference Day One

9:00 am Login & Networking


Join our morning virtual coffee chat room to meet with your fellows and get the conversation going!

9:20 am Opening Remarks from the Chair

  • Monica Wang Principal Technology Lead, Project and Program Management, Scientific Informatics, Takeda Pharmaceuticals

Advanced Computational Biology & Bioinformatics 2.0

9:30 am Computer-Aided Drug Discovery & Chaperone Proteins as Drug Targets


  • How to use computer-aided drug discovery, fragment based screening, homology modelling and docking for inhibitors identification
  • Using fragment in-silico screening for drug rational design
  • Novel AI approaches: Machine learning and deep learning to identify novel candidates

10:00 am Immunogenicity & Antigenicity-Based T-Cell & Epitopes Identification for Design of Biologic Therapeutics

  • Monica Wang Principal Technology Lead, Project and Program Management, Scientific Informatics, Takeda Pharmaceuticals
  • Hyelim Cho Group Lead, Immunogenicity, Takeda Pharmaceuticals


  • In silico analysis as an integrative approach for the assessment of immunogenicity in the pre-clinical phase
  • In vitro validation study to offer predictive insights on immunogenicity of biotherapeutics
  • Remaining challenges to offer better predictive insights to mitigate clinical immunogenicity risk of biotherapeutics

10:40 am Machine Learning on Genomics Data for Therapeutic Discovery

  • Jimeng Sun Chief AI Scientist, IQVIA & Professor, University of Illinois Urbana-Champaign, IQVIA; University of Illinois Urbana-Champaign


  • Identify diverse ML applications in genomics spanning the therapeutics pipeline and provide pointers as to the latest methods and resources
  • Discuss concise ML problem formulation to help ML researchers navigate technical challenges for ML method innovations
  • Present an overview of ML application use cases and show where they can extend to novel use cases

11:10 am Morning Speed Networking


Connect with your industry peers through our virtual speed networking tool. You will be paired up with a fellow colleague for a 5-minute ice breaking conversation!

11:30 am Large-Scale Computational Analysis Reveals MHC-II-Based Antibody Gene Personalization Features


  • Exploring datasets to follow patterns, trends, and associations in antibody genetics across individuals
  • Studying the molecular pathways of somatic hypermutation and antibody immunogenicity

12:00 pm Chemical Profiling of Proteins Interacting with DNA Structures in Live Cells


DNA–protein interactions regulate critical biological processes. Identifying proteins that bind to specific, functional genomic loci is essential to understand the underlying regulatory mechanisms on a molecular level.

In this presentation I will describe our co-binding-mediated protein profiling (CMPP) strategy developed together with the University of Cambridge to investigate the interactome of DNA secondary structures in native chromatin.

CMPP involves cell-permeable, functionalized ligand probes that bind endogenous secondary structures such as G-quadruplexes (G4s) and subsequently crosslink to co-binding interacting proteins in situ. I will first illustrate the bioinformatics approaches that went into investigating the robustness of CMPP by proximity labelling of a G4 binding protein in vitro.

Employing this approach in live cells, we then identified hundreds of putative interacting proteins from various functional classes. Next, we confirmed a high binding affinity and selectivity for several newly discovered interactors in vitro, and we validated the functionally important candidate SMARCA4 in cellular chromatin using an independent approach.

This work provides a chemical strategy to map protein interactions to specific nucleic acid features and discover new epigenetic targets in living cells.

12:30 pm Physics-Based Simulations to Investigate Developability for Therapeutic Antibodies


  • The development of in silico models for stability prediction proves challenging due to the limited availability of adequately sized experimental data necessary for model training
  • The emergence of novel therapeutic modalities can further restrict the sizes of datasets, and thus the transferability and generalizability of predictive methods
  • Physics-based approaches that are not limited on training data offer a unique advantage by providing profound mechanistic insight into the physical and chemical stability, examples of such class of computations will be presented

1:00 pm Comparison of Antibody Interfaces – Lessons for the Design of Bispecific Antibodies


  • Comparison CH1-CL and CH3-CH3 interfaces
  • Exploring stabilities of antibodies
  • CDR loop interactions as key determinants for pairing preferences in bispecific antibodies

1:30 pm Networking Lunch


Grab a bite while joining our virtual lunch chat room, or use this opportunity to visit our Partners’ Virtual Booths

2:00 pm Featurization of Protein-Protein Interfaces for Machine Learning in Large Molecule Drug Discovery


  • Atomic-level characterization of protein interfaces can be computed from all experimental, and improved modelled, structures to generate a large dataset (4×10^6) suitable for machine learning input
  • We created PiEx, a fully automated platform for protein:protein interface analysis, that allows navigation and exploration via interactive visuals of the large data
  • PiEx generates novel insights from native and crystal interfaces, as exemplified in B-cell and T-cell receptor studies

Novel Drug Development Approach Using Computer-Aided Approach

2:30 pm CDD Vault: The Source of Truth for all Your Data Management Needs


  • CDD provides a modern approach to drug discovery research informatics trusted globally by thousands of leading researchers.
  • The CDD Vault is a hosted biological and chemical database management system that helps project teams securely manage, analyze and present data for biotech companies, CROs, academic labs, research hospitals, agrochemical and consumer goods companies.
  • CDD Vault is a flexible, configurable system used to register all different types of entities; examples of registered biological entities will be shown.

3:00 pm Developing Novel Computational Models to Predict Antibody Stability

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


  • In silico modelling to predict physical stability of antibodies in the early-stage design
  • Predicting viscosity and aggregation propensity at high concentration using machine learning
  • Combining coarse-grained simulations with hydrodynamic calculations to predict antibody viscosity

3:30 pm Panel Discussion: Drug Discovery & Development Involving Machine Learning & Artificial Intelligence

  • Sukanya Sasmal Senior Scientist, Computational Structural Biology, Sanofi Pasteur
  • Andy Nuzzo Computational Biologist, GSK
  • Katrina Lexa Associate Director & Senior Scientist, Denali Therapeutics
  • Jimeng Sun Chief AI Scientist, IQVIA & Professor, University of Illinois Urbana-Champaign, IQVIA; University of Illinois Urbana-Champaign
  • Vinodh Kurella Senior Scientist,


  • Limited data is the key limitation for using AI and ML for drug discovery and development
  • How to improve the validation tasks for your lead therapeutic
  • Identifying data challenges: scale, growth, diversity, and uncertainty of data
  • The challenges of predicting efficacy and immunogenicity

4:10 pm Afternoon Speed Networking


Take this opportunity to meet with our expert partners for 1-2-1 meetings to find out about their latest innovations.

4:30 pm Update on the Latest ML-based Methods for Biologics Engineering

  • Yu Qiu Digital Biologics Advanced Applications Lead, Large Molecule Research, Sanofi


  • Novel machine learning algorithms along with increasing accumulation of data empowers digital innovation for biologics engineering
  • Large Molecule Research in collaboration with Digital & Data Science at Sanofi, benchmarked state-of-the-art methods and developed ddG prediction tools
  • This preliminary result demonstrated promising ability of ML-based models in biologics engineering

5:00 pm Accelerating Antibody Informatics with Machine Learning


  • CDR-H3 is a fingerprint region of each antibody, and its conformation is key for antigen recognition
  • Examining various kinds of machine learning methods (Random Forest, Decision Tree, Light GBM, SVM, MLP) for the prediction of CDR-H3’s structure type
  • Recurrent neural network and convolutional neural network in combination with reinforcement learning for the de novo design of developable antibody sequences

5:30 pm Generative AI for Peptide Drug Discovery

  • Ho Leung Ng Associate Professor, Kansas State University


  • Applications of generative AI for peptides and peptidomimetics
  • Applications to SARS-CoV-2 3CL protease and neurotensin receptor