Conference Day One

9:00 am Login & Networking

Synopsis

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

Advanced Computational Biology & Bioinformatics 2.0

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

Synopsis

  • 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

Synopsis

  • 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 Large-Scale Computational Analysis Reveals MHC-II-Based Antibody Gene Personalization Features

Synopsis

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

11:10 am Morning Speed Networking

Synopsis

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 Computational Strategies for Targeted Protein Degradation

Synopsis

  • This session will explore how computational tools can support targeted protein degradation, opening up previously undruggable opportunities.

12:00 pm Physics-Based Simulations to Investigate Developability for Therapeutic Antibodies

Synopsis

  • 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

12:30 pm Comparison of Antibody Interfaces – Lessons for the Design of Bispecific Antibodies

Synopsis

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

1:00 pm Networking Lunch

Synopsis

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

Synopsis

  • 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 Developing Novel Computational Models to Predict Antibody Stability

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

Synopsis

  • 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

3:00 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

Synopsis

  • Exploring how pharma are utilising ML with limited data
  • Discovering how to improve speed of validation of the drug target and optimization of the drug structure design
  • Identifying data challenges: scale, growth, diversity, and uncertainty of data
  • Predicting complex biological properties, such as the efficacy and adverse effects of compounds

3:45 pm Afternoon Speed Networking

Synopsis

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

4:15 pm Update on the Latest ML-based Methods for Biologics Engineering

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

Synopsis

  • 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

4:45 pm Accelerating Antibody Informatics with Machine Learning

Synopsis

  • 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

5:15 pm Generative AI for Peptide Drug Discovery

  • Ho Leung Ng Associate Professor, Kansas State University

Synopsis

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

5:45 pm Chair’s Closing Remarks & End of Day One