8:00 am Check-In & Coffee
9:00 am Workshop A
Is your Hit a Hit? Investigating Experimental Strategies for Reducing & Understanding False Positive Hits
Synopsis
As drug discovery begins to steer towards more and more difficult targets and go beyond the low hanging fruit, identifying any false positives quickly proves necessary to preventing wasting both resources and time. From ensuring your data quality is appropriate and robust prior to starting on a campaign to prevent troubleshooting halfway through, all the way to designing an appropriate hit triage to follow your primary assay and addressing all potential false positive mechanisms; join this workshop to strategize your approach to false positives.
This workshop will cover:
- Developing triage strategies to maximize confidence in your hits, with particular emphasis on small molecule phenotypic screening
- Recognizing blind spots in your screening assays and avoiding misinterpretations
- Prioritizing hits and understanding when it is appropriate to resynthesize your compound
12:00 pm Lunch Break & Networking
1:00 pm Workshop B
Harnessing Artificial Intelligence & Machine Learning to Improve Speed, Robustness & Efficacy of the Hit ID Process
Synopsis
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:
- Exploring both the success stories and learnings of the application of AI to HTS, FBDD and DEL screening
- Training an AI model for novel applications through leveraging data & finding potential hit ID strategies
- Mastering AI for biochemical and biophysical assay applications to streamline assay development to enhance efficiency and accuracy of hit identification
- Utilizing AI to uncover and drug previously “undruggable” targets
- 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