Open Challenges in AI for Molecular Design: Representation, Experimental Alignment, and Oracle Reliability

Explore the frontiers of AI in molecular design through this insightful lecture by Connor Coley from MIT. Delve into the complexities of applying machine learning and artificial intelligence to molecular discovery workflows. Examine the current limitations in computational methods that hinder experimental success in the field. Investigate key challenges including the representation of molecular structures beyond covalent bonding, the misalignment between generative design and experimental execution, and the reliance on fallible oracles in virtual screening and generative design processes. Gain a deeper understanding of the ongoing efforts to bridge the gap between AI-driven molecular design and real-world experimental outcomes in this hour-long presentation from the Simons Institute's AI≡Science series.

Open challenges in AI for molecular design: representation, experimental alignment, and...