8:50 am Chair’s Opening Remarks

Data Driven Discovery – Optimizing Research Through Data

9:00 am Case Study – Investigating Advanced AI Technology for Protein Design

  • Kevin Hua Senior Manager AI/Machine Learning, Bayer

Synopsis

• Looking at developments in protein design creating novel biomolecules with desired functions
• Exploring the advanced AI technology and methodology
• Explaining the value behind the project and the potential in creating new biomaterials

9:30 am Advancing Drug Discovery Using Knowledge-Graph Based ML and AI

  • Natnael Hamda AI Research Fellow, Clinical Pharmacology, Astellas Pharma

Synopsis

• Generating insights by connecting different data sets and applying AI/ML on top
• Creating advantages through leveraging publicly available ‘big data’ sources for ML/AI practice
• Providing a framework for more efficient, effective and approachable drug discovery

10:00 am Using AI to Scale Drug Discovery for Rare Diseases

Synopsis

• The team at Healx are combining artificial intelligence with world-leading drug discovery expertise to find treatments for the >400 million individuals whose lives are affected by a rare disease. 95% of those conditions do not currently have an approved treatment.
• Healx’s innovative AI platform, Healnet, identifies potential opportunities to repurpose existing drugs that can be evaluated experimentally and moved to clinical trials more quickly than traditional drug discovery methods allow.
• The exploitation of AI technology in different stages of the drug discovery process enables the Healx team to scale the approach to bring benefit to a large number of patients with many different conditions

10:30 am Virtual Speed Networking & Morning Break

11:30 am Case Study – Use of AI & ML Approaches to Extract Features From MRI Scans to Discover Drug Targets for NASH

  • Paul Nioi Senior Director Research , Alnylam Pharmaceuticals

Synopsis

• How AI can assist in diagnosis and provide a better understanding of the disease
• Proposing candidate drug targets and predicting responses to therapeutic candidates
• Quickly identifying hurdles and methods to overcome them

12:00 pm Case Study – Using Neural Networks to Predict & Optimize Protein Production in Bioreactors

Synopsis

• What are the challenges faced in modeling a bioreactor?
• Finding an ideal feed formula and interpreting the results
• Lessons learned and how they are being leveraged

12:30 pm Networking Lunch

1:30 pm Using Machine Learning and Artificial Intelligence to Prioritize Rare & Orphan Disease Drug Candidates

  • Steve Smith Principal Computational Biology Data Scientist, Teva Pharmaceuticals

Synopsis

• Why rare and orphan disease research and drug discovery can benefit greatly from AI technologies
• Example of how AI has been used to enhance therapeutic development for rare and orphan diseases
• Further exploring AI potential to improve patient outcomes

Long Term AI/ML Industry Changes Resulting From COVID-19

2:00 pm Case Study: Pivoting a General AI Drug Discovery Approach to Tackle the Urgent Need for COVID-19

Synopsis

• Overview of Recursion’s approach that successfully predicts several early clinical outcomes
• The benefit of generalized AI-driven assays for rapid drug discovery
• How a machine learning drug-discovery pipeline pivots to respond to the COVID-19 pandemic

2:30 pm Afternoon Networking Break

3:00 pm Panel Discussion – AI ML Lessons Learned Throughout Lockdown

Synopsis

• Discussing how the industry coped during lockdown, looking at the important lessons learned for the future, analysing the failures, and identifying possible silver linings for the AI ML community

3:45 pm Case Study – The AI Approach Being Used by The AAPM Coronavirus Taskforce During the Pandemic

Synopsis

• Understanding the taskforce’s mission and how AI driven solutions and other precision medicine techniques represent an approach not yet applied to COVID-19
• Analysing the contribution and outcomes of AI usage so far
• What have been the challenges and lessons learned along the way? How will this project benefit AI ML going forward?

4:00 pm End of Day 1