#MachineLearning

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 2: AI That Sees, Measures, and Assigns Chirality

From interpretive expertise to multimodal analytical inference Summary Determining a molecule’s configuration, or resolving its enantiomers on a column, has traditionally meant comparing experimental data against a computed reference  a process gated by quantum chemistry and by expert judgement. Machine learning is now compressing that comparison from hours to seconds and, in chiral chromatography, is …

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Episode 1: Teaching Machines Molecular Handedness

Why chirality has to be built into a molecular representation before any model can learn it Episode 1 of 5 Summary Two enantiomers can differ by an order of magnitude in potency, yet to most molecular machine learning models they are the same molecule. This is not a data problem that more training examples will …

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