What are the key advances, opportunities and challenges in graph-based drug design?

Discovering new promising molecule candidates that could translate into effective drugs is a key scientific pursuit. However, factors such as the vastness and discreteness of the molecular search space pose a formidable technical challenge in this quest. AI-driven generative models can effectively learn from data, and offer hope to streamline drug design. 

In an article published in Current Opinion in Structural Biology, we review state of the art in generative models that operate on molecular graphs. We also shed light on some limitations of the existing methodology and sketch directions to harness the potential of AI for drug design tasks going forward. 

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