AI can now design a promising molecule in seconds — but a molecule you can’t synthesize is just a picture. Today’s digest is about the other half of discovery: teaching machines to plan how to make things. Segler & Waller showed deep nets could ‘resolve reactivity conflicts and prioritize the most suitable transformation rules,’ hitting ‘95%’ top-10 retrosynthesis. Coley et al. went knowledge-free, ranking disconnections by ‘molecular similarity … without the need to encode any chemical knowledge.’ Then Segler et al.’s landmark used ‘Monte Carlo tree search and symbolic artificial intelligence’ to solve ‘almost twice as many molecules, thirty times faster.’ Schwaller et al.’s Molecular Transformer solved the forward problem as machine translation, ‘above 90%’ top-1 with calibrated uncertainty. Genheden et al.’s open-source AiZynthFinder made it a tool anyone can run ‘in less than 10 s.’ And Coley et al. closed the loop with ‘a robotically controlled experimental platform,’ making 15 drug-like compounds. Plan, predict, and press go.
