#ArtificialIntelligence

Episode 4: AI for making and separating the right stereoisomer

Where chirality aware machine learning has actually been tested against experiment Summary This is the part of the field with a genuine experimental track record. Models have proposed catalysts, enzyme variants, reactions and resolving agents that were then made and tested, and that worked. A model designed resolution screen resolved three of six previously unseen …

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Episode 3: Predicting What Chirality Does

Recognising two stereoisomers is one problem; predicting how they diverge is a much harder one Summary The reason to build chirality-aware models is not to classify stereocentres but to predict consequences: which enantiomer binds, which is cleared faster, which carries the toxicity. This is where the evidence base is thinnest. Chirality-aware architectures win on benchmark …

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