AI/ML in Hit Identification – Exploring the Current State of the Field, Key Challenges, and Future Directions
Time: 1:10 pm
day: Pre-Conf Day
Details:
Artificial intelligence is widely regarded as the up and coming pathway in drug discovery, due to its potential to both accelerate and refine the hit identification process. AI offers novel avenues to hit ID to save time and ultimately costs through revolutionizing screening strategies by accurately
predicting suitable candidates and potential hits through a deep knowledge of available datasets. Despite this burgeoning field proving to be a huge area of global interest, the seamless application of AI to current screening strategies is something to be further elucidated.
This workshop will cover:
Part 1 – Yifan Song
- Computational tools and high-throughput screening for reducing T cell immunogenicity
- The existing experimental data and computational tools focus on identification of T cell epitopes, but the outcome is not sensitive and quantitative enough for small changes in sequences
- We have been developing new high-throughput screening platform and in silico tools better suited for protein engineering
- Exploring the best practices for combining AI driven tools and different experimental assessments to optimize biologics and reduce immunogenicity risks
Part 2 – Woody Sherman
- Grand Challenges for Computers in Drug Discovery
- We will present a set of grand challenges for computational tools in small molecule drug discovery
- This unifying framework will enable the community to more readily evaluate the effectiveness of computational methods that have the potential to aid in the discovery of novel therapeutics
- Insights presented here stem from experts that span drug hunters, method developers, and thought leaders in addition to lessons learned from our internal drug discovery efforts
