The application of AI to target protein folding and identification is rapidly evolving, with tools like AlphaFold, and emerging hybrid models offering unprecedented insights into protein structure and function. Yet, the diversity of available technologies, each with distinct strengths and limitations, presents a challenge for teams seeking to integrate these tools into their discovery workflows. This workshop offers a foundational and critical evaluation of the current AI landscape for target enablement, helping attendees navigate the complex trade-offs between accuracy, scalability, interpretability, and experimental validation. 

Participants will explore:

• The comparative strengths and weaknesses of leading AI tools for predicting protein folding, binding surfaces, and post-translational modifications 
• Tool performance for hard-to-drug targets such as GPCRs, transcription factors, and RNA-binding proteins 
• Strategies for integrating AI predictions with experimental methods (e.g. cross-linking, cryo-EM, FRET) to validate cryptic binding pockets and dynamic conformations 
• Case studies on the use of AI in target identification and the role of pharmacophore modeling 
• Practical considerations for selecting the right AI tool based on target class, data availability, and downstream application in small molecule design