Capturing the Promise of Machine Learning: Overcoming Key Challenges to Accelerate Drug Discovery
There is no question that AI is already having a positive impact in many phases of the drug discovery and development pipeline, but there are also still many challenges to fully realizing the anticipated potential of AI.
This roundtable discusses the most urgent functional gaps hindering drug discovery teams from fully capturing the promise of machine learning to accelerating early-stage drug discovery for critical diseases. We look at recent case studies and explore how key aspects such as data, algorithms, and molecular descriptors can be optimized to improve success.
Areas of discussion include:
- What are the highest priority opportunities, blockers and risks?
- How can we improve data access, preparation, management and diversity to support algorithm training and model validation to better predictions?
- Is there overlooked potential in optimizing molecular fingerprints?
- What is the next big emerging frontier in algorithmic approaches?
- Are there different challenges in development of novel molecules versus drug repurposing efforts?
- What impact are partnerships and collaborative projects having on advancing progress? What challenges are they facing?
- Todd Wills, Managing Director, CAS Services
- Kathy Gibson, Innovation & Investment Director, Pistoia Alliance
- Pat Walters, SVP Computation, Relay Therapeutics
- Govinda Bhisetti, Principal Investigator and Head of Computational Chemistry, Biogen