7:50 am Check-In & Morning Coffee
8:50 am Chair’s Opening Remarks
Optimizing mRNA Sequence Design Algorithms to Incorporate Target Product Profile & Predict Translation Efficiency
9:00 am Showcasing Future Directions From an Algorithm for Optimized mRNA Design to Improve Stability & Immunogenicity
Synopsis
- Learnings from our algorithm LinearDesign for optimal mRNA design and showcasing clinical data
- Future directions utilizing the algorithm for circular RNA design
- Harnessing deep learning methods for optimal RNA design
9:30 am Highlighting a Deep Learning Guided Optimization of Translation Efficiency for mRNA Vaccine Development
Synopsis
- Delivering mRNA vaccines from a high protein yield to stimulate an effective immune response
- Training RiboNN, a deep learning model, to predict translation efficiency (a determinant of protein yield) among numerous cell types
- Utilizing RiboNN to guide the design of translation-optimized mRNA therapeutics
10:00 am Morning Break & Speed Networking
Synopsis
Our dedicated speed networking session is the perfect opportunity to have in-depth conversations and forge long-lasting connections with fellow technical experts working in computational RNA design & delivery.
11:00 am Designing Onco-Selectively Translated mRNA by Using Machine Learning
Synopsis
- How to precisely target oncotherapeutics to cancer cells only, while avoiding healthy tissue, can broaden a drug’s therapeutic window and avoid harmful off-target effects?
- Integrating multiple public and proprietary data sources (including proteomics and transcriptomics) to identify mRNA sequence elements that enable selective translation only in cells of interest
- Outlining how our robust computational pipeline can simultaneously identify these sequence elements while generating testable hypotheses about associated biomarkers for patient stratification
Addressing the Big Data Conundrum Through Dataset Generation to Optimize Model Prediction for RNA Therapeutics
11:30 am Panel Discussion: Enhancing Training for RNA Therapeutics Computational Prediction Through High Quality Dataset Generation & Cross Learnings from the Small Molecule World
Synopsis
- What are the key principles for experimental dataset generation to advance training for better computational model prediction of RNA therapeutic properties?
- What model architectures are currently being used outside of RNA discovery and what can be learnt from their training dataset for optimized predictive outcome?
- What is the minimal data set required with data quality preserved, and where can data from public repositories be leveraged to enhance model training?
12:30 pm Networking Lunch
Driving Advances in LNP discovery Through Computational Optimization & Prediction Tools for More Cost-Effective, Targeted & Safe RNA Delivery
1:30 pm An LNP Discovery Flywheel: Massively Parallel High Resolution In Vivo LNP Screening & Predictive Modeling
Synopsis
- Moving discovery screens in vivo captures the most relevant physiology
- Measuring the multiome across single cells and tissues gives unprecedented insight in primary screens
- Showcasing rich screen data provides for a predictive modeling virtuous
2:00 pm Afternoon Break & Poster Session
Synopsis
As AI/ML and computational tools drive the advancement of the next wave of discovery of RNA therapeutics, collaboration and mutual learning is more important than ever. Join our dedicated poster session to share your latest data and have an exclusive look into what your peers are working on!
Spotlighting RNA DesignConsiderations to Influence the Future of Process Development & Manufacturing
2:45 pm Optimizing mRNA Expression, Half-Life & Manufacturability With CodonCracker™
Synopsis
- Outlining the current state of open-source and commercially available optimizers
- Implementing CodonCracker™ optimization models and search algorithms
- Harnessing experimental data that tests model performance on multiple axes
3:15 pm Harnessing Computational Tools to Generate Synthetic, Expression Optimized & Target Sequence Dependent UTRs Increasing Manufacturability Success
Synopsis
- How to collect, connect and analyze multivariate, proprietary, and public mRNA data to build new models?
- Identifying key methods to generate and test in silico synthetic generated UTRs and sequence designs
- Incorporating UTR and sequence design recommendations to obtain sequences with increased expression levels in vitro as well as ease manufacturing