AI in Chirality – Chiralpedia

Episode 5: From Generative Stereochemistry to Autonomous Chiral Discovery

Author(s): Valliappan Kannappan and Chandramouli R
AI in Chirality
#AI, #ChiralDrugDiscovery, #chirality, #Robotic Chemistry, #stereochemistry, GenerativeAI Leave a Comment
November 10, 2026 November 10, 2026

From Generative Stereochemistry to Autonomous Chiral Discovery Summary A closed loop for chirality would design a stereochemically defined molecule, plan a route, execute it, measure enantiomeric excess, and decide what to run next. Every link now exists somewhere in the literature. None have been joined end to end for a stereochemical objective. The weakest links …

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Episode 4: AI for making and separating the right stereoisomer

Author(s): Valliappan Kannappan and Chandramouli R
AI in Chirality
#ArtificialIntelligence, #AsymmetricSynthesis, #Biocatalysis, #ChiralResolutiomn, #Enantioselectivity, #MachineLearning Leave a Comment
November 4, 2026 November 10, 2026

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

Author(s): Valliappan Kannappan and Chandramouli R
AI in Chirality
#ADMET, #ArtificialIntelligence, #ChiralDrugDiscovery, #MoleculaModelling, #Stereoselectivity Leave a Comment
October 21, 2026 November 10, 2026

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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Episode 2: AI That Sees, Measures, and Assigns Chirality

Author(s): Valliappan Kannappan and Chandramouli R
AI in Chirality
#AI, #ChiralAnalysis, #ChiralChromatography, #ChiralSpectroscopy, #MachineLearning, #NMR Leave a Comment
September 15, 2026 November 10, 2026

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

Author(s): Valliappan Kannappan and Chandramouli R
AI in Chirality
#AI, #ChiralMolecules, #MachineLearning, #MolecularChirality, #MolecularRepresentation, #stereochemistry Leave a Comment
September 7, 2026 November 10, 2026

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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